REVIEW | doi:10.20944/preprints202105.0338.v1
Online: 14 May 2021 (14:04:25 CEST)
The understanding of weather and climate extremes provides academics, decision makers, international development agencies, nongovernmental organizations and civil society the necessary information for monitoring and giving early warning to prevent or minimize the risks associated with weather related hazards. Different researches were carried out to provide vital information that will further enhance the assessment of vulnerability and its impacts. Lack of proper understanding of weather and climate extremes was realized to be responsible for the huge and devastating losses that could have being averted or minimized over the past decades. Different countries and institutions have put in place a number of ways to increase sensitization and awareness of weather extremes. This became necessary in order to reduce the losses associated with these extremes both on local and regional scales.
ARTICLE | doi:10.20944/preprints202101.0112.v1
Subject: Earth Sciences, Atmospheric Science Keywords: CMIP6; extreme precipitation; model evaluation; east Africa
Online: 6 January 2021 (11:37:37 CET)
This paper presents an analysis of precipitation extremes over the East African region. The study employs six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) to evaluate possible climate change. Observed datasets and CMIP6 simulations and projections are employed to assess the changes during the two main rainfall seasons of March to May (MAM) and October to December (OND). The study evaluated the capability of CMIP6 simulations in reproducing the observed extreme events during the period 1995 – 2014. Our results show that the multi-model ensemble (herein referred to as MME) of CMIP6 models can depict the observed spatial distribution of precipitation extremes for both seasons, albeit with some noticeable exceptions in some indices. Overall, MME's assessment yields considerable confidence in CMIP6 to be employed for the projection of extreme events over the study area. Analysis of extreme estimations shows an increase (decrease) in CDD (CWD) during 2081 – 2100 relative to the baseline period in both seasons. Moreover, SDII, R95p, R20mm, and PRCPTOT demonstrate significant OND estimates compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while reduction is observed over the Tanzania region. The increase in projected extremes during two main rainfall seasons poses a significant threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are encouraged to examine the underlying physical features modulating the occurrence of extremes incidences projected for relevant policies.
ARTICLE | doi:10.20944/preprints201903.0002.v1
Subject: Life Sciences, Microbiology Keywords: Extreme Pathways, Nutrient Removal, C. vulgaris, P.aeruginosa
Online: 1 March 2019 (07:20:22 CET)
Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs confining in them a high amount of nutrients and organics contaminants. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pesudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients such as Phosphorus (inorganic phosphorous and phosphate) and Nitrogen (nitrates and ammonia). Theoretical yields for Phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO4/ g DW of C. vulgaris, 19.53 mmol of inorganic Phosphorus /g DW of C. vulgaris and 4.90 mmol of inorganic Phosphorus/ g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for Nitrogen removal are 10.3 mmol of NH3/g DW of P. aeruginosa and 7.19 mmol of NO3 /g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.
ARTICLE | doi:10.20944/preprints202005.0444.v1
Subject: Mathematics & Computer Science, Computational Mathematics Keywords: restricted Boltzmann machine; contrastive divergence; extreme learning machine; online sequential extreme learning machine; autoencoders; deep belief network; deep learning
Online: 27 May 2020 (08:18:39 CEST)
Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.
ARTICLE | doi:10.20944/preprints202208.0540.v1
Subject: Earth Sciences, Atmospheric Science Keywords: urban waterlogging risk; extreme rain; drainage capacity; Shanghai
Online: 31 August 2022 (08:55:36 CEST)
Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model(RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk.
ARTICLE | doi:10.20944/preprints202104.0753.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Convolutional extreme learning machine; Deep learning; Multimedia analysis
Online: 28 April 2021 (15:31:14 CEST)
Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.
REVIEW | doi:10.20944/preprints202007.0583.v1
Subject: Life Sciences, Genetics Keywords: genetic association studies; extreme phenotype; genetic epidemiology; tinnitus
Online: 24 July 2020 (13:43:00 CEST)
Exome sequencing has been commonly used in rare diseases by selecting multiplex families or singletons with an extreme phenotype (EP) to search for rare variants in coding regions. The EP strategy covers both extreme ends of a disease spectrum and it has been also used to investigate the contribution of rare variants to heritability in complex clinical traits. We have conducted a systematic review to find evidence supporting the use of EP strategies to search for rare variants in genetic studies of complex diseases, to highlight the contribution of rare variation to the genetic structure of multiallelic conditions. After performing the quality assessment of the retrieved records, we selected 19 genetic studies considering EP to demonstrate genetic association. All the studies successfully identified several rare variants, de novo mutations and many novel candidate genes were also identified by selecting an EP. There is enough evidence to support that the EP approach in patients with an early onset of the disease can contribute to the identification of rare variants in candidate genes or pathways involved in complex diseases. EP patients may contribute to a better understanding of the underlying genetic architecture of common heterogeneous disorders such as tinnitus or age-related hearing loss.
ARTICLE | doi:10.20944/preprints201905.0292.v1
Subject: Medicine & Pharmacology, Sport Sciences & Therapy Keywords: CrossFit; high-intensity functional training; Extreme conditioning programs
Online: 24 May 2019 (11:36:10 CEST)
The purpose of this study was to assess if self-regulation of intensity based on rating of perceived exertion (RPE) is a reliable method to control the intensity of metabolic conditioning of functional-fitness session. In addition, the relationship between RPE and changes in heart rate and lactate responses was also analyzed. Eight male participants (age 28.1 ± 5.4 years; body mass 77.2 ± 4.4kg; VO2max: 52.6 ± 4.6 mL·(kg·min)−1) completed three randomly sessions (5 to 7 days apart) under different conditions: (1) all-out (ALL); (2) self-regulation of intensity based on a RPE of 6 (hard) on the Borg CR-10 scale (RPE6); and (3) a control session. Rate of perceived exertion, LAC and HR response were measured pre, during and immediately after the sessions. The RPE and LAC during the ALL-OUT sessions were higher (p ≤ 0.05) than the RPE6 and control sessions for all the analyzed time points during the sessions. Regarding HR, the 22 min area under the curve of HR during ALL-OUT and RPE6 sessions were significantly higher (p ≤ 0.05) than the control session. The average number of repetitions was lower (p ≤ 0.05) for the RPE6 session (190.5 ± 12.5 repetitions) when compared to the ALL session (214.4 ± 18.6 repetitions). There was a significant correlation between RPE and LAC (p = 0.001; r = 0.76; very large) and number of repetitions during the session (p = 0.026; r = 0.55; large). No correlation was observed between RPE and HR (p = 0.147; r = 0.380). These results indicate that self-regulation of intensity of effort based on RPE may be a useful tool to control exercise intensity during a metabolic conditioning session of functional-fitness.
ARTICLE | doi:10.20944/preprints201812.0331.v1
Subject: Physical Sciences, Atomic & Molecular Physics Keywords: gas analyzers; optical sensors; TDLAS; extreme learning machine
Online: 28 December 2018 (05:05:28 CET)
In this work, a tailored extreme learning machine (ELM) algorithm to enhance the overall robustness of gas analyzer based on the tunable diode laser absorption spectroscopy (TDLAS) method is presented. The ELM model is tailored through activation function selection, input weight and bias searching, and cross validation method to address the analyzer robustness issues for industrial process analysis field application. The two particular issues are the inaccurate gas concentration measurement caused by the process gas background components variation, and the inaccurate spectra shift calculation caused by spectral interference. By using our algorithm, the concentration error is reduced by one order of magnitude over a much larger stream pressure and component range compared with that obtained by classical least square (CLS) fitting methods based on reference curves. Additionally, it is shown that with our algorithm, the wavelength shift accuracy is improved to less than 1 count over 1000 counts spectra length. In order to test the viability of our algorithm, a trace ethylene (C2H4) TDLAS analyzer with coexisting methane was implemented, and its experimental measurements support analyzing robustness enhancement effect.
ARTICLE | doi:10.20944/preprints202105.0395.v1
Subject: Engineering, Automotive Engineering Keywords: Inclusion; size distribution; population distribution function; extreme value theory
Online: 17 May 2021 (16:57:34 CEST)
The increasing demand for higher inclusion cleanliness levels motivates the control over the formation and evolution of inclusions in the steel production process. In this work, the evolution of the chemical composition and size distribution of inclusions throughout a slab production process of Al-killed steel, including ladle furnace (LF) treatment and continuous casting (CC), was followed. The initial solid Al2O3 and Al2O3-MgO inclusions were modified to liquid Al2O3-CaO-MgO inclusions during LF treatment. The evolution of the size distributions during LF treatment was associated with the growth and removal of inclusions, as new inclusions were not created after the deoxidation process, according to a population density function (PDF) analysis. Additionally, the size distributions tended to be similar as the LF treatment progressed regardless of their initial features, whereas they differed during CC. Analysis of the upper tails of the distributions through generalized extreme values theory showed that inclusion distributions shifted from larger to smaller sizes as the process progressed. There were great changes in the distributions of large inclusions throughout the LF treatment and between the end of the LF treatment and the start of the CC process. Additionally, distributions of large inclusions differed at the end of the LF treatment, whereas such differences decreased as CC progressed.
ARTICLE | doi:10.20944/preprints202104.0577.v1
Subject: Earth Sciences, Atmospheric Science Keywords: WASP-Index; Climate change; Projections; Extreme precipitation; Iberian Peninsula
Online: 21 April 2021 (12:17:36 CEST)
The WASP-Index is computed over Iberia for three monthly timescales in 1961-2020, based on an observational gridded precipitation dataset (E-OBS), and in 2021-2070, based on bias-corrected precipitation generated by a six-member climate model ensemble from EURO-CORDEX, under RCP4.5 and RCP8.5. The WASP performance in identifying extremely dry or wet events, reported by the EM-DAT disaster database, is assessed for 1961–2020. An overall good agreement between the WASP spatial patterns and the EM-DAT records is found. The areolar mean values revealed an upward trend in the frequency of occurrence of intermediate-to-severe dry events over Iberia, which will be strengthened in the future, particularly for the 12m-WASP intermediate dry events under RCP8.5. Besides, the number of 3m-WASP intermediate-to-severe wet events is projected to increase, mostly the severest events under RCP4.5, but no evidence was found for an increase in the number of more persistent (12m-WASP) wet events under both RCPs. Despite important spatial heterogeneities, an increase(decrease) of the intensity, duration, and frequency of occurrence of the 12m-WASP intermediate-to-severe dry(wet) events is found under both scenarios, mainly in the southernmost regions of Iberia, thus becoming more exposed to prolonged and severe droughts in the future, corroborating the results from previous studies.
ARTICLE | doi:10.20944/preprints201811.0340.v1
Subject: Earth Sciences, Atmospheric Science Keywords: damaged area; direct economic loss; disaster; drought; extreme precipitation
Online: 15 November 2018 (04:26:41 CET)
Understanding the distribution in drought and floods plays an important role in disaster risk management. The present study aims to explore the trends in the standardized precipitation index and extreme precipitation days in China, as well as to estimate the economic losses they cause. We found that in the Northeast China, northern of North China and northeast of Northwest China were severely affected by drought disasters (average damaged areas were 6.44 million hectares) and the most severe drought trend was located in West China. However, in the north of East China and Central China, the northeastern of the Southwest China was severely affected by flood disasters (average damaged areas were 3.97 million hectares) and the extreme precipitation trend is increasing in the northeastern of the Southwest China. In the Yangtze River basin, there were increasing trends in terms of drought and extreme precipitation, especially in the northeastern of the Southwest China, where accompanied by severe disaster losses. By combining the trends in drought and extreme precipitation days with the distribution of damaged areas, we found that the increasing trend in droughts shifted gradually from north to south, especially in the Southwest China, and the increasing trend in extreme precipitation gradually shifted from south to north.
ARTICLE | doi:10.20944/preprints201809.0239.v2
Subject: Earth Sciences, Environmental Sciences Keywords: El Nino Southern Oscillation; ENSO, El Nino extreme events;
Online: 7 November 2018 (16:30:41 CET)
Observed ENSO statistics exhibits a non-gaussian behavior, which is indicative of the presence of nonlinear processes. In this paper we use the Recharge Oscillator model (ROM), a largely used Low-Order Model (LOM) of ENSO, as well as methodologies borrowed from the field of statistical mechanics to identify which aspects of the system may give rise to nonlinearities that are consistent with the observed ENSO statistics. In particular, we are interested in understanding whether the nonlinearities reside in the system dynamics or in the fast atmospheric forcing. Our results indicate that one important dynamical nonlinearity often introduced in the ROM cannot justify a non-gaussian system behavior, while the nonlinearity in the atmospheric forcing can instead produce a statistics similar to the observed. The implications of the non-Gaussian character of ENSO statistics for the frequency of extreme El Nino events is then examined.
ARTICLE | doi:10.20944/preprints202008.0089.v1
Subject: Earth Sciences, Geology Keywords: Deep Neural Network; Extreme Gradient Boosting; Random Forest; Landslide Susceptibility
Online: 4 August 2020 (11:13:02 CEST)
Landslides impact on human activities and socio-economic development especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides i.e. topographic, hydrologic, soil, forest, and geologic factors are prepared from various sources based on availability and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performing field survey. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories content 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models i.e. Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.757 and the testing accuracy is 0.74. Similarly, training accuracy of XGBoost is 0.756 and testing accuracy is 0.703. The prediction of DNN revealed acceptable agreement between susceptibility map and the existing landslides with training and testing accuracy of 0.855 and 0.802, respectively. The results showed that, the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area
ARTICLE | doi:10.20944/preprints202001.0010.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: hydrocarbon gases; solubility; extreme learning machines; electrolyte solution; predicting model
Online: 2 January 2020 (04:39:59 CET)
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.
ARTICLE | doi:10.20944/preprints201808.0385.v1
Subject: Engineering, Mechanical Engineering Keywords: sparse reconstruction, extreme learning machines, sensors, SVD, POD, compressive sensing
Online: 21 August 2018 (16:18:06 CEST)
Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into data-driven models. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches encode the physics into an underlying sparse basis space that spans the manifold to generate well-posedness. To achieve this, one commonly uses generic orthogonal Fourier basis or data specific proper orthogonal decomposition (POD) basis to reconstruct from sparse sensor information at chosen locations. Such a reconstruction problem is well-posed as long as the sensor locations are incoherent and can sample the key physical mechanisms. The resulting inverse problem is easily solved using $l_2$ minimization or if necessary, sparsity promoting $l_1$ minimization. Given the proliferation of machine learning and the need for robust reconstruction frameworks in the face of dynamically evolving flows, we explore in this study the suitability of non-orthogonal basis obtained from Extreme Learning Machine (ELM) auto-encoders for sparse reconstruction. In particular, we assess the interplay between sensor quantity and sensor placement for a given system dimension for accurate reconstruction of canonical fluid flows in comparison to POD-based reconstruction.
ARTICLE | doi:10.20944/preprints201804.0312.v1
Subject: Earth Sciences, Other Keywords: natural hazards; weather disasters; hydrometeorological fatalities; flooding; tornadoes; extreme temperatures
Online: 24 April 2018 (08:51:45 CEST)
Texas ranks first in number of natural hazard fatalities in the United States (U.S.). Based on data culled from the National Climatic Data Center databases from 1959 to 2016, the number of hydrometeorological fatalities in Texas have increased over the 58-year study period, but the per capita fatalities have significantly decreased. Spatial review found that flooding is the predominant hydrometeorological disaster in a majority of the Texas counties located in “Flash Flood Alley” and accounts for 43% of all hydrometeorological fatalities in the state. Flooding fatalities are highest on “Transportation Routes” followed by heat fatalities in “Permanent Residences”. Seasonal and monthly stratification identifies Spring and Summer as the deadliest seasons, with the month of May registering the highest number of total fatalities dominated by flooding and tornado fatalities. Demographic trends of hydrometeorological disaster fatalities indicated that approximately twice as many male fatalities occurred during the study period than female fatalities, but with decreasing gender disparity over time. Adults are the highest fatality risk group overall, children most at risk to die in flooding, and the elderly at greatest risk of heat-related death.
ARTICLE | doi:10.20944/preprints202104.0585.v1
Subject: Earth Sciences, Atmospheric Science Keywords: remote sensing rainfall; extreme precipitation indices; gridded rainfall products; monsoon rainfall
Online: 21 April 2021 (15:39:34 CEST)
This work focuses on the analysis of the performance of satellite-based precipitation products for monitoring extreme rainfall events. Five precipitation products are inter-compared and evaluated in capturing indices of extreme rainfall events during 1998-2019 considering four indices of extreme rainfall. Satellite products show a variable performance, which in general indicates that the occurrence and amount of rainfall of extreme events can be both underestimated or overestimated by the datasets in a systematic way throughout the country. Also, products that consider the use of ground truth data have the best performance.
ARTICLE | doi:10.20944/preprints202012.0335.v1
Subject: Social Sciences, Accounting Keywords: air quality; extreme weather; MA-MSD method; investor sentiment; behavioral finance
Online: 14 December 2020 (13:13:36 CET)
We investigate the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we use dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative influence on the equity returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative impacts on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the weather effect of the abnormal temperature on the stock returns is greater in severe bearish market. Whereas the effect of the air pollution on the stock returns is greater in the bullish market. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analyzing these effects in a very volatile emerging market such as the Shenzhen stock market.
ARTICLE | doi:10.20944/preprints202008.0171.v1
Subject: Behavioral Sciences, Social Psychology Keywords: Air quality; Extreme weather; MA-MSD method; Investor sentiment; Behavioural finance
Online: 7 August 2020 (04:08:44 CEST)
We investigate the impact of air quality and weather on the stock market returns of the Shenzhen Exchange. To capture the air quality and weather effects, we apply dummy variables generated by applying a moving average and moving standard deviation. Our study provides several interesting results. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative effects on the Shenzhen stock returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative effects on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the more the Shenzhen stock returns drop, the greater the effect of the abnormal temperature is. Whereas, the more the Shenzhen stock returns increase, the greater the effect of the abnormal air quality is. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analysing these effects in a very volatile emerging market such as the Shenzhen stock market.
ARTICLE | doi:10.20944/preprints201608.0200.v1
Subject: Engineering, Civil Engineering Keywords: climate change; GCMs’; RCPs’; downscaling; temperature; precipitation; extreme events; SWAT; discharge
Online: 24 August 2016 (10:16:40 CEST)
Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.
REVIEW | doi:10.20944/preprints202203.0052.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Mangroves; Drivers; Anthropogenic activities; Climate change; Extreme events; Wetlands; Interaction; Aquaculture; Agriculture
Online: 3 March 2022 (04:39:35 CET)
Globally mangrove forests are substantially declining and a globally synthesized database of the drivers of deforestation and drivers’ interaction is scarce. Here we synthesized the key social-ecological drivers of global mangrove deforestation by reviewing about two hundred published scientific studies over the last four decades (from 1980 to 2021). Our focus was on both natural and anthropogenic drivers with gradual and abrupt impacts and their geographic ranges of effects and how these drivers interact. We also summarized the patterns of global mangrove coverage decline between 1990 and 2020 and identified the threatened mangrove species and their geographic ranges. Our consolidated studies reported a 8,600 km2 decline in the global mangrove coverage between 1990 and 2020 with the highest decline occurring in South and Southeast Asia (3870 km2). We could identify 11 threatened mangrove species, two of which are critically endangered (Sonneratia griffithii and Bruguiera hainseii). Our reviewed studies pointed to aquaculture and agriculture as the predominant driver of global mangrove deforestation though the spatial distribution of their impacts varied. Gradual climate variations, i.e. seal-level rise, long-term precipitation and temperature changes and driven coastline erosion, constitute the second major group of drivers. Our findings underline a strong interaction across natural and anthropogenic drivers with the strongest interaction between the driver groups aquaculture and agriculture and industrialization and pollution. Our results suggest prioritizing globally coordinated empirical studies linking drivers and mangrove changes and a global development of policies for mangrove conservation.
REVIEW | doi:10.20944/preprints202008.0369.v2
Subject: Life Sciences, Other Keywords: Extreme capsule; uncinate fasciculus; IFOF; ventral pathway of language; bottle neck; DTI
Online: 7 September 2020 (10:26:24 CEST)
On review of neuroscience literature extreme capsule considered as a whiter matter tract. Nevertheless it is not clear that extreme capsule itself is a association fiber pathway or is o bottleneck for passing other association fiber. By a systematic search with investigating anatomical position, dissection, connectivity and cognitive role of extreme capsule it can be argued that extreme capsule probably is a bottleneck for passing uncinated fasciculus (UF) and inferior fronto – occipital fasciculus(IFOF), And its different role of language processing is duo to different tract that passing it.
Subject: Medicine & Pharmacology, Other Keywords: COVID-19; SARS-CoV2; extreme epidemiology response; population at risk; case fatality
Online: 12 April 2020 (14:08:15 CEST)
Objectives: COVID-19, a respiratory disease caused by SARS-COV2 and transmitted from person-to-person through viral droplets remains a global pandemic. There is a need to understand the transmission modes, populations at risk, and how to mitigate the spread and case fatality in the United States (US) and globally. The current study aimed to assess the global COVID-19 transmission and case fatality, examine similar parameters by countries and determine evidence-based practice in extreme epidemiology response in epidemic curve flattening and case fatality reduction. Methods: A cross-sectional ecologic design was used to assess the preexisting data on confirmed COVID-19 cases and mortality in March 2020 from the CDC, WHO, Worldodomter, and STATISTA. A rapid assessment between March 23rd and 31st, 2020, was utilized for the extreme epidemiology response. The case fatality, termed fatality proportion, was examined using mortality in relation to confirmed cases involving the world, United States of America (USA), United Kingdom (UK), Italy, France, Spain, China, Germany, India and South Korea. Results: The COVID-19 is a global pandemic, with the US as the epicenter for transmission, representing 20.9% of all confirmed cases worldwide, while Italy is the epicenter for case fatality, 30.6% of mortality as at 03/31/ 2020. The fatality proportion (FP) in Italy was 11.4%, Spain (8.8%), France (6.8%) and UK (6.4%). Despite the increased number of confirmed cases, the lowest FP was observed in Germany (0.96%) and South Korea (1.66%). There is increasing linear tends in transmission in the US, R2=0.97 as well as positive daily percentage change, ranging from 1.27% to 20.5%. Conclusions: The USA remains the epicenter for COVID-19 transmission, while Italy is the epicenter for case fatality. The observed relatively low case fatality in Germany and South Korea is due to an “extreme epidemiology” response through the application of Wuhan, China’s early data on COVID-19 transmission control measures and optimized patient care. These data are suggestive of relaxing the clinical guidelines in the United States in COVID-19 testing, application of contact tracing and testing, case isolation and most importantly enhancing resources for case management and social and physical distancing globally, hence epidemic curve flattening and case fatality reduction.
ARTICLE | doi:10.20944/preprints202001.0213.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: glomerular filtration rate; Brenner hypothesis; extreme low birth weight infants; renal outcome
Online: 19 January 2020 (05:12:19 CET)
Different cohort studies documented a lower glomerular filtration rate (GFR) in former extremely low birth weight (ELBW, <1000 g) neonates throughout childhood when compared to term controls. The current aim is to pool these studies to describe the GFR pattern over the pediatric age range. To do so, we conducted a systematic review on studies reporting on GFR measurements in former ELBW cases while GFR data of healthy age-matched controls included in these studies were co-collected. Based on 248 hits, 6 case-control and 3 cohort studies were identified, with 444 GFR measurements in 380 former ELBW cases (median age 5.3-20.7 years). The majority were small (17-78 cases) single center studies, with heterogeneity in GFR measurement (inulin, Cystatin C or creatinine estimated GFR formulae) tools. Despite this, the median GFR (ml/kg/1.73m2) within case-control studies was consistently lower (-13, range -8 to -25%) in cases, so that a relevant minority (15-30%) has a eGFR<90 mgl/kg/1.73m2). Consequently, this pooled analysis describes a consistent pattern of reduced eGFR in former ELBW cases throughout childhood. Research should focus on perinatal risk factors for impaired GFR and long-term outcome, but is hampered by single center cohorts, study size, and heterogeneity of GFR assessment tools.
ARTICLE | doi:10.20944/preprints201905.0052.v1
Subject: Earth Sciences, Geophysics Keywords: sea level rise; coastal flood hazard; storm surge; extreme tidal level; GIS
Online: 6 May 2019 (10:57:09 CEST)
Portugal Mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be protected from natural coastal hazard, namely extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard in the Continental Atlantic coast of Portugal Mainland was evaluated for 2025, 2050 and 2100 in the whole coastal extension with different sea level scenarios for different extreme event return periods and due to SLR. A coastal flooding probabilistic map was produced based on the developed methodology using Geographic Information Systems (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on flood probabilistic bases through five probability intervals of 20% of amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).
ARTICLE | doi:10.20944/preprints201811.0265.v1
Subject: Earth Sciences, Other Keywords: CAMELS; flood frequency; hydrological signatures; extreme value theory; random forests; spatial modelling
Online: 12 November 2018 (04:59:22 CET)
The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, important explanatory variables have not been found for the GEV shape parameter, despite its critical significance, which stems from the fact that it determines the behaviour of the upper tail of the distribution. Here we examine the nature of the shape parameter by revealing its relationships with basin attributes. We use a dataset that comprises information about daily streamflow and forcing, climatic indices, topographic, land cover, soil and geological characteristics of 591 basins with minimal human influence in the contiguous United States. We propose a framework that uses random forests and linear models to find (a) important predictor variables of the shape parameter and (b) an interpretable model with high predictive performance. The process of study comprises of assessing the predictive performance of the models, selecting a parsimonious predicting model and interpreting the results in an ad-hoc manner. The findings suggest that the shape parameter mostly depends on climatic indices, while the selected prediction model results in more than 20% higher accuracy in terms of RMSE compared to a naïve approach. The implications are important, since incorporating the regression model into regional flood frequency analysis frameworks can considerably reduce the predictive uncertainties.
ARTICLE | doi:10.20944/preprints201703.0229.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Can Tho city; extreme event; urban flooding; water quality monitoring; water pollution
Online: 31 March 2017 (09:50:47 CEST)
Water pollution associated with flooding is one of the major problems in cities in the global South. However, studies of water quality dynamics during flood events are not often reported in literature, probably due to difficult conditions for sampling during flood events. Water quality parameters in open water (canals, rivers, and lakes), floodwater on roads and water in sewers have been monitored during the extreme fluvial flood event on 7 October 2013 in Can Tho city, Vietnam. This is the pioneering study of urban flood water pollution in real time in Vietnam. The results showed that water quality is very dynamic during flooding, especially at the beginning of the event. In addition, it was observed that the pathogen and contaminant levels in the floodwater are almost as high as in sewers. The findings show that population exposed to flood water runs a health risk that is nearly equal to that of being in contact with sewer water. Therefore the people of Can Tho not only face physical risk due to flooding, but also exposed to health risk.
ARTICLE | doi:10.20944/preprints202110.0302.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: load forecasting; extreme learning machine (ELM); ant lion optimization (ALO) ；parameter optimization; model.
Online: 21 October 2021 (09:34:56 CEST)
The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.
ARTICLE | doi:10.20944/preprints202105.0325.v1
Subject: Engineering, Automotive Engineering Keywords: Stochastic modelling; Climate change; Streamflow; El Nino/Southern Oscillation (ENSO), Extreme events modelling
Online: 14 May 2021 (11:43:06 CEST)
Water is essential to all life-forms including various ecological, geological, hydrological, and climatic processes/activities. With changing climate, associated El Nino/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration (EV) processes across the globe. Changes in P and EV patterns are highly sensitive to temperature variation and thus also affecting natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework HMM_GP that integrates a Hidden Markov Model with a Generalised Pareto distribution for simulating synthetic flow sequences. The GP distribution within HMM_GP model is aimed to improve the model's efficiency in effectively simulating extreme events. This paper further investigated the potentials of Generalised Extreme Value Distribution (EVD) coupled with an HMM model within a regression-based scheme for associating impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic has been thoroughly assessed for their suitability to generate/predict synthetic river flows sequences for a set of future climatic projections. The new modelling schematic can be adapted for a range of applications in the area of hydrology, agriculture and climate change.
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: risk factors; second primary cancer (SPC); colorectal cancer; classification techniques; extreme gradient boosting
Online: 15 December 2019 (13:30:09 CET)
In Taiwan, colorectal cancer is ranked second and third in terms of mortality and cancer incidence, respectively. In addition, medical expenditures related to colorectal cancer are considered to be the third highest. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five data mining classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. In total, 4,287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.
ARTICLE | doi:10.20944/preprints202104.0722.v1
Subject: Earth Sciences, Atmospheric Science Keywords: extreme precipitation; Mediterranean region; Pyrenees; return period; teleconnection indices; weather type.; Backward trajectory; IVT
Online: 27 April 2021 (13:00:21 CEST)
Mountain systems within the Mediterranean region, e.g. the Pyrenees, are very sensitive to climate change. In the present study, we quantified the magnitude of extreme precipitation events and the number of days with torrential precipitation (daily precipitation ≥ 100 mm) in all the rain gauges available in the Pyrenees for the 1981-2015 period, analyzing the contribution of the synoptic scale in this type of events. The easternmost (under the Mediterranean influence) and north-westernmost (under Atlantic influence) areas of the Pyrenees registered the highest number of torrential events. The heaviest events are expected in the eastern part, i.e. 400 mm day-1 for a return period of 200 years. Northerly advections over the Iberian Peninsula, which present a low zonal index, i.e. im-plying a stronger meridional component, give rise to torrential events over the western Pyrenees; and easterly advections favour extreme precipitation over the eastern Pyrenees. The air mass travels a long way, from the east coast of North America, bringing heavy rainfall to the western Pyrenees. In the case of the torrential events over the eastern Pyrenees, the air mass causing the events in these areas is very short and originates in the Mediterranean Basin. The NAO index has no influence upon the occurrence of torrential events in the Pyrenees, but these events are closely related to certain Mediterranean teleconnections such as the WeMO
ARTICLE | doi:10.20944/preprints202005.0386.v1
Subject: Keywords: remaining useful life; c-mapss; extreme learning machine; prognostic and health management; neural networks
Online: 24 May 2020 (16:24:08 CEST)
This work can be considered as a first step of designing a future competitive data-driven approach for remaining useful life prediction of aircraft engines. The proposed approach is an ensemble of serially connected extreme learning machines. The results of prediction of the first networks are scaled and fed to the next networks as an additive features to the original inputs. This feature mapping allows increasing the correlation of training inputs with their targets by holding new prior knowledge about the probable behavior of the target function. The proposed approach is evaluated under remaining useful estimation using a set of “time-varying” data retrieved from the public dataset C-MAPSS (Commercial Modular Aero Propulsion System Simulation) provided by NASA. The prediction performances are compared to basic extreme learning machine and proved the effectiveness of the proposed methodology.
ARTICLE | doi:10.20944/preprints202001.0310.v1
Subject: Mathematics & Computer Science, Applied Mathematics Keywords: mathematical modeling; characteristic points; extreme pressure; hydraulic jump; pressure fluctuations; standard deviation; stilling basin
Online: 26 January 2020 (07:32:50 CET)
Pressure fluctuations beneath hydraulic jumps downstream of Ogee spillways potentially damage stilling basin beds. This paper deals with the extreme pressures underneath free hydraulic jumps along a smooth stilling basin. The experiments were conducted in a laboratory flume. From the probability distribution of measured instantaneous pressures, the pressures with different non-exceedance probabilities (P*a%) could be determined. It was verified that the maximum pressure fluctuations, as well as the negative pressures, are located at the positions closest to the spillway toe. The minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of P*a% related to the characteristic points along the basin, and different Froude numbers. To benchmark, the results, the dimensionless forms of mean pressures, standard deviations, and pressures with different non-exceedance probabilities were assessed. It was found that an existing methodology can be used to interpret the present data, and pressure distribution in similar conditions, by using a new third-order polynomial relationship for the standard deviation (σ*X) with the determination coefficient (R2) equal to 0.717. It was verified that the new optimized adjustment gives more accurate results for the estimation of the maximum extreme pressures than the minimum extreme pressures.
ARTICLE | doi:10.20944/preprints201907.0345.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: Alzheimer’s Disease; Extreme Gradient Boosting; Deep Residual Learning; conolutional neural networks; machine learning; dementia
Online: 31 July 2019 (04:33:43 CEST)
Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's Disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted CDR presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4,139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine Learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
ARTICLE | doi:10.20944/preprints201808.0551.v1
Subject: Engineering, Energy & Fuel Technology Keywords: Non-intrusive Load Monitoring; Machine Learning; Deep Modeling; Extreme Learning Machine; Data Driven Approach.
Online: 31 August 2018 (15:44:51 CEST)
Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.
ARTICLE | doi:10.20944/preprints201706.0079.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: wearable; photoplethysmography; spectral kurtosis; extreme learning machine (ELM) regression; respiration rate; cardiovascular diseases (CVD)
Online: 16 June 2017 (10:45:32 CEST)
In this paper, we present the design of a wearable photoplethysmography (PPG) system, R-band for acquiring the PPG signals. PPG signals are influenced by the respiration or breathing process and hence can be used for estimation of respiration rate. R-Band detects the PPG signal that is routed to a Bluetooth low energy device such as a nearbyplaced smartphone via microprocessor. Further, we developed an algorithm based on Extreme Learning Machine (ELM) regression for the estimation of respiration rate. We proposed spectral kurtosis features that are fused with the state-ofthe-art respiratory-induced amplitude, intensity and frequency variations-based features for the estimation of respiration rate (in units of breaths per minute). In contrast to the neural network (NN), ELM does not require tuning of hidden layer parameter and thus drastically reduces the computational cost as compared to NN trained by the standard backpropagation algorithm. We evaluated the proposed algorithm on Capnobase data available in the public domain.
ARTICLE | doi:10.20944/preprints201903.0075.v1
Subject: Physical Sciences, Astronomy & Astrophysics Keywords: extreme weather events; heat waves; sun-earth relationships; sun and weather; space weather and extreme atmospheric events; global atmospheric anomalies; SEP events and weather; SEP and NAO; gulf stream and heat waves
Online: 6 March 2019 (11:01:50 CET)
In the past two decades the world experienced an exceptional number of unprecedented extreme weather events, some causing major human suffering and economic damage, such as the March 2012 heat event, which was called “Meteorological March Madness.” From the beginning of space era a correlation of solar ﬂares with pressure changes in atmosphere within 2–3 days or even less was reported. In this study we wanted to test the possible relation of highly warm weather events in North-East America with Solar Energetic Particle (SEP) events. For this reason we compared ground temperatures TM in Madison, Wisconsin, with energetic particle fluxes P measured by the EPAM instrument onboard the ACE spacecraft. In particular, we elaborated case events and the results of a statistical study of the SEP events related with the largest (Dst ≤ −150nT) Coronal Mass Ejection (CME)-induced geomagnetic storms, between with the years 1997–2015. The most striking result of our statistical analysis is a very significant positive correlation between the highest temperature increase. ΔTM and the time duration of the temperature increase ΔTM (r = 0.8, p <0.001) at “winter times” ( r = 0.5, p , 0.01 for the whole sample of 26 SEP examined events). The time response of TM to P was found to be in general short (a few days), but in the case of March 2015, during a gradual P8 increase, a cross-correlation test indicated highest c.c. within 1 day (p < 0.05). The March 2012 “meteorological anomaly” was elaborated in the case of South-East Europe, where, beside a period of strong winds and rainfall (6-13.3.2012), intense precipitation in North-East Greece (Alexandroupoli) were found to be correlated with distinct high energy flux enhancements. A rough theoretical interpretation is discussed for the space—atmospheric extreme weather relationship we found. However, much work should be done to achieve early warning of space weather dependent extreme meteorological events. Such future advances in understanding the relationships between space weather and extreme atmospheric events would improve atmospheric models and help people’s safety, health and life.
ARTICLE | doi:10.20944/preprints202203.0337.v1
Subject: Earth Sciences, Geoinformatics Keywords: landslide susceptibility; stacking ensemble; machine learning; random forest; gradient boosting decision tree; extreme gradient boosting
Online: 25 March 2022 (03:43:32 CET)
The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.
ARTICLE | doi:10.20944/preprints202112.0192.v1
Subject: Engineering, Industrial & Manufacturing Engineering Keywords: End-point phosphorus content; Deep extreme learning machine; Sparrow search algorithm; Trigonometric substitution; Cauchy mutation
Online: 10 December 2021 (15:04:59 CET)
：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.
CONCEPT PAPER | doi:10.20944/preprints202112.0081.v1
Subject: Engineering, General Engineering Keywords: Agriculture; Extreme Heat; Climate Change; Grandview; Edge Computing; 5G; IoT; Drone Imagery; LiDAR; Decision framework
Online: 6 December 2021 (15:19:50 CET)
The US pacific northwest recorded its highest temperature in late June 2021. The three-day stretch of scorching heat had a devastating effect on not only the residents of the state, but also on the crops thus impacting the food supply-chain. It is forecasted that streaks of 100-degree temperatures will become common. Farmers will have to adapt to the changing landscape to preserve their crop yield and profitability. A research collaborative consisting of researchers and academicians in Eastern Washington led by a pioneering startup has setup a 16.9-acre Honeycrisp Apple Smart Orchard in Grandview, WA as a laboratory to study the environmental and plant growth factors in real-time using modern computational tools and techniques like IoT (Internet of Things), Edge and Cloud Computing, and Drone and LiDAR (Light Detection and Ranging) imaging. The computational analysis is used to develop guidelines for precision agriculture for orchard blocks to address plant growth issues scientifically and in a timely fashion. The analysis also helps in creating risk-mitigation strategies for severe weather events while helping prepare farmers to maximize crop yield and profitability per acre. I was fortunate to gain access to the terabytes of farm data related to the weather, soil, water, tree, and canopy health, to analyze and formulate recommendations for the farmers that can be adopted nationwide for different crops and weather conditions. This paper discusses the different streams of farm data that were analyzed (ex. soil moisture, soil water potential, and sap flow) and the development of the framework to use data to convert insights into actionable steps. For example, the use of sensors can inform a farmer that their level of soil water potential is below threshold in a specific patch of the orchard, prompting them to turn on irrigation for the patch instead of the whole orchard. I estimate that using an IoT-sensor-based decision framework discussed in this paper, growers can save up to 55% of their water costs for the season. Using these insights, farmers can better manage their irrigation resources and labor, thus maximizing their crop yield and profits.
ARTICLE | doi:10.20944/preprints202004.0164.v2
Subject: Biology, Ecology Keywords: drought; climate variability; resilience; resistance; estuary; fish; extreme events; Delta Smelt; Chinook Salmon; Largemouth Bass
Online: 23 July 2020 (10:30:03 CEST)
Many estuarine ecosystems and the fish communities that inhabit them have undergone substantial changes in the past several decades, largely due to multiple interacting stressors that are often of anthropogenic origin. Few are more impactful than droughts, which are predicted to increase in both frequency and severity with climate change. In this study, we examined over five decades of fish monitoring data from the San Francisco Estuary, California, U.S.A, to evaluate the resistance and resilience of fish communities to disturbance from prolonged drought events. High resistance was defined by the lack of decline in species occurrence from a wet to a subsequent drought period, while high resilience was defined by the increase in species occurrence from a drought to a subsequent wet period. We found some unifying themes connecting the multiple drought events over the fifty-year period. Pelagic fishes consistently declined during droughts (low resistance), but exhibit a considerable amount of resiliency and often rebound in the subsequent wet years. However, full recovery does not occur in all wet years following droughts, leading to permanently lower baseline numbers for some pelagic fishes over time. In contrast, littoral fishes seem to be more resistant to drought and may even increase in occurrence during dry years. Based on the consistent detrimental effects of drought on pelagic fishes within the San Francisco Estuary and the inability of these fish populations to recover in some years, we conclude that freshwater flow remains a crucial but not sufficient management tool for the conservation of estuarine biodiversity.
ARTICLE | doi:10.20944/preprints202006.0369.v1
Subject: Life Sciences, Biophysics Keywords: temperature extreme; warm climate; low-and middle-income economies; COVID-19; mortality; mixed effect modelling
Online: 30 June 2020 (11:38:15 CEST)
We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and temperature extreme (difference between maximum and minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration after the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, temperature extreme was significantly and positively correlated with mortality in the low-and middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed effect in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggest that warm climate may reduce the mortality rate in high-income economies but in low and middle-income countries temperature extreme may increase the mortality risk.
ARTICLE | doi:10.20944/preprints201704.0012.v1
Subject: Engineering, Civil Engineering Keywords: Unmanned Aerial Vehicle (UAV); UAV-photogrammetry; Structure From Motion (SfM); cut slope; extreme topography; landslide
Online: 3 April 2017 (18:34:22 CEST)
UAV photogrammetry development during the last decade has allowed to catch information at a very high spatial and temporal resolution from terrains with very difficult or impossible human access. This paper deals with the application of these techniques to study and produce information of very extreme topography which is useful to plan works on this terrain or monitoring it over the time to study its evolution. The methodology stars with the execution of UAV flights on the cut slope studied, one with the cam vertically oriented and other at 45º respect that orientation. Ground control points (GCPs) and check points (CPs) were measured for georeference and accuracy measurement purposes. Orthophoto was obtained projecting on a fitted plane to a studied surface. Moreover, since a digital surface model (DSM) is not able to represent faithfully that extreme morphology, information to project works or monitoring it has been derived from the point cloud generated during the photogrammetric process. An informatics program was developed to generate contour lines and cross sections derived from the point cloud, which was able to represent all terrain geometric characteristics, like several Z coordinates for a given planimetric (X, Y) point. Results yield a root mean square error (RMSE) in X, Y and Z directions of 0.053 m, 0.070 m and 0.061 m respectively. Furthermore, comparison between contour lines and cross sections generated from point cloud with the developed program on one hand and those generated from DSM on other hand, shown that the former are capable of representing terrain geometric characteristics that the latter cannot. The methodology proposed in this work has been shown as an adequate alternative to generate manageable information, as orthophoto, contour lines and cross sections, useful for the elaboration, for example, of projects for repairing or maintenance works of cut slopes with extreme topography.
ARTICLE | doi:10.20944/preprints202206.0006.v1
Subject: Earth Sciences, Oceanography Keywords: Sea Level Rise; coastal flooding; JPM; Gumbel; exceedance; extreme value statistics; flood return period; sea-defences
Online: 1 June 2022 (05:58:45 CEST)
AbstractLocal estimates of coastal flood risk are required for coastal planning and development, including the location and design of sea-defences and coastal buildings, such as harbours and associated infrastructure. This paper discusses the use of three parameters associated with estimating such risks; the flood return period, the instantaneous flood probability and the flood design risk, and it describes the mathematical background for their derivation. The discussion is extended to include the effects of sea level rise and how it can be incorporated into the calculations. Flood height can vary quite rapidly with distance along the coast, being affected by coastal topology, which may magnify or diminish the tidal and surge effects. Similarly land heave influences the local effects of sea level rise and can be influenced by water extraction, tectonic movements and melting ice. Tide gauge measurements provide a local historical record from which the various parameters can be retrieved. This paper discusses the algorithms used to derive these measures from tide-gauge records. The figures have been derived for four tide gauges located on the UK east coast.
ARTICLE | doi:10.20944/preprints201803.0088.v1
Subject: Engineering, Civil Engineering Keywords: extreme water level; hydrodynamic model; Monte Carlo; joint probability; model calibration and verification; Danshuei River system
Online: 12 March 2018 (07:56:58 CET)
Estimates of extreme water level return periods in river systems are crucial for hydraulic engineering design and planning. Recorded historical water level data of Taiwan’s rivers are not long enough for traditional frequency analyses when predicting extreme water levels for different return periods. In this study, the integration of a one-dimensional flash flood routing hydrodynamic model with the Monte Carlo simulation was developed to predict extreme water levels in the Danshuei River system of northern Taiwan. The numerical model was calibrated and verified with observed water levels using four typhoon events. The results indicated a reasonable agreement between the model simulation and observation data. Seven parameters, including the astronomical tide and surge height at the mouth of the Danshuei River and the river discharge at five gauge stations, were adopted to calculate the joint probability and generate stochastic scenarios via the Monte Carlo simulation. The validated hydrodynamic model driven by the stochastic scenarios was then used to simulate extreme water levels for further frequency analysis. The design water level was estimated using different probability distributions in the frequency analysis at five stations. The design high-water levels for a 200-year return period at Guandu Bridge, Taipei Bridge, Hsin-Hai Bridge, Da-Zhi Bridge, and Chung-Cheng Bridge were 2.90 m, 5.13 m, 6.38 m, 6.05 m, and 9.94 m, respectively. The estimated design water levels plus the freeboard are proposed and recommended for further engineering design and planning.
REVIEW | doi:10.20944/preprints202205.0227.v1
Subject: Earth Sciences, Geophysics Keywords: Coastal storm; Wind wave; Storm surge; Extreme coastal water level; Boulder dynamics; Geomorphological proxy; Interdisciplinary climate research
Online: 17 May 2022 (10:28:58 CEST)
In this review, the potential of an emerging field of interdisciplinary climate research, that is the Coastal Boulder Deposits (CBDs) as natural archives for intense storms, is explored with particular reference to the Mediterranean region. First, the identification of the pertinent scientific articles was performed by using Web of Science (WoS) engine. Thus, the selected studies have been analysed to feature CBDs produced and/or activated during the last half century. Then, the meteorological events responsible to the literature reported cases were analysed in some details using the web archives of the Globo-Bolam-Moloch model cascade. The study of synoptical and local characteristics of the storms involved in the documented cases of boulder production/activation proved useful to assess the suitability of selected sites as geomorphological storm proxies. It is argued that a close and fruitful collaboration involving several scientific disciplines is required to develop this climate research field.
ARTICLE | doi:10.20944/preprints202112.0187.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: renewable energy; bayes estimation; beta distribution; lognormal distribution; compound inverse Rayleigh distribution; extreme values; safety; wind power
Online: 10 December 2021 (14:03:52 CET)
The paper deals with the Compound Inverse Rayleigh distribution, shown to constitute a proper model for the characterization of the probability distribution of extreme values of wind-speed, a topic which is gaining growing interest in the field of renewable generation assessment, both in view of wind power production evaluation and the wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model - already proven to be a valid model for extreme wind-speeds - by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity to interpret different field data is illustrated, also by means of numerous numerical examples based upon real wind speed measurements. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. In practice, the method allows to express one’s prior beliefs both in terms of parameters, as customary, and/or in terms of probabilities. The results of a large set of numerical simulations – using typical values of wind-speed parameters - are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.
ARTICLE | doi:10.20944/preprints201910.0349.v2
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: hybrid machine learning; extreme learning machine (ELM); radial basis function (RBF); breast cancer; support vector machine (SVM)
Online: 24 February 2020 (04:10:49 CET)
Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.
ARTICLE | doi:10.20944/preprints202002.0086.v1
Subject: Engineering, Civil Engineering Keywords: Buildings; earthquake safety assessment; extreme events; urban sustainability; seismic 16 assessment; rapid visual screening; reinforced concrete buildings
Online: 6 February 2020 (10:50:33 CET)
Earthquake is among the most devastating natural disasters causing severe economic, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainable development through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider Rapid Visual Screening (RVS) method which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 11 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable while EMPI and IITK-GGSDMA provide for more accurate and practical estimation, respectively.
ARTICLE | doi:10.20944/preprints201901.0146.v1
Subject: Engineering, Control & Systems Engineering Keywords: Biochemical oxygen demand (BOD); Cuckoo search algorithm (CSA); Extreme learning machine (ELM); Soft sensor; Wastewater treatment process
Online: 15 January 2019 (09:13:22 CET)
It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.
ARTICLE | doi:10.20944/preprints202207.0280.v1
Subject: Engineering, General Engineering Keywords: Hyperspectral Technology; Non-destructive Testing; Soybean; Machine Learning; Support Vector Machine; Extreme Gradient Boosting; Tree-structured Parzen Estimator
Online: 19 July 2022 (07:12:32 CEST)
Soybean with insignificant differences in appearance have large differences in their internal physical and chemical components, therefore follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybean for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in 4 categories is collected; Ten features is selected by extreme gradient boosting algorithm from 203 hyperspectral bands in range 400 to 1000 nm; A Gaussian radial basis kernel function support vector machine with optimization by the Tree-structured Parzen Estimator algorithm is built as TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset which is 9.786% higher for vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model.
ARTICLE | doi:10.20944/preprints202201.0210.v1
Subject: Behavioral Sciences, Other Keywords: adventure sport; extreme sport; ecological dynamics; transdisciplinary; form of life; skill; skill development; decision-making; freeriding; avalanche education
Online: 14 January 2022 (11:51:24 CET)
The last few decades have witnessed a surge of interest in adventure sports, and an emerging research focus on these activities. However, recent conceptual analyses and scientific reviews have highlighted a major, fundamental question that remains unresolved: what constitutes an adventure sport (and are they ‘sports’ at all)? Despite several proposals for definitions, the field still seems to lack a shared conceptualization. This deficit may be a serious limitation for research and practice, restricting the development of a more nuanced theoretical explanation of participation and prac-tical implications within and across adventure sports. In this article we address another crucial question, how can adventure sports be better understood for research and practice? We briefly summarize previous definitions to address evident confusion and lack of conceptual clarity in the discourse. Alternatively, we propose how an ecological perspective on human behaviors, as in-teractions with the environment, may provide an appropriate conceptualization to guide and enhance future research and practice, using examples from activities such as freeride skiing / snowboarding, white-water kayaking, climbing, mountaineering and the fields of sport science, psychology and avalanche research and education. We draw on ecological dynamics as a trans-disciplinary approach to discuss how this holistic framework presents a more detailed, nuanced, and precise understanding of adventure sports.
ARTICLE | doi:10.20944/preprints202112.0143.v1
Subject: Earth Sciences, Geoinformatics Keywords: satellite data; machine learning; data calibration; thermal time; growing degree days; Extreme Gradient Boosting; crop yield; crop monitoring
Online: 8 December 2021 (15:42:11 CET)
Timely crop yield forecasts at national level are substantial to support food policies, to assess agricultural production and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments (such as DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.
ARTICLE | doi:10.20944/preprints202106.0025.v1
Subject: Engineering, Automotive Engineering Keywords: Adequate drainage structures; Rainfall IDF Curve relationship; predicted peak rate of runoff (Qlogy); Gumbel’s Extreme Value Distribution Method.
Online: 1 June 2021 (11:14:54 CEST)
Due to the increase in the emission of greenhouse gases, the hydrologic cycle is being altered on the daily basis. This has affected the variations in relations of intensity, duration, and frequency of rainfall events. Intensity Duration Frequency (IDF) curves describe the relationship between rainfall intensity, rainfall duration and return period. IDF curves are one of the most often applied implements in water resource engineering, in areas such as for operating, planning and designing of water resource projects, or for numerous engineering projects aimed at controlling floods. In particular, IDF curves for precipitation answer problems of improper drainage systems or conditions and extreme characters of precipitation which are the main cause of floods in Nyabugogo catchment. This study aims to establish Rainfall IDF empirical equations, curves and hydrological discharge (predicted peak rate of runoff (Qlogy)) equations for eight Districts that will be used for designing an appropriate and sustainable hydraulic structures for controlling flood to reduce potential loss of human and aquatic life, degradation of water, air and soil quality and property damage and economic lessen caused by flood in Nyabugogo catchment. However Goodness of Fit tests revealed that Gumbel’s Extreme-Value Distribution method appears to have the most appropriate fit compared with Pearson type III distribution for validating the Intensity-Duration-Frequency curves and equations through the use of daily annual for each meteorological station. The findings of the study show that the intensity of rainfall increases with a decrease in rainfall duration. Additionally, a rainfall of every known duration will have a higher intensity if its return period is high, while the predicted peak rate of runoff (Qlogy) increases also with an increase in the intensity of rainfall.
ARTICLE | doi:10.20944/preprints202110.0152.v1
Subject: Medicine & Pharmacology, General Medical Research Keywords: extreme weather; heat waves; anvironment and public healt; aged; older adults; social behaviour; interpersonal relation; social isolation; mortality; lonelliness
Online: 11 October 2021 (08:59:11 CEST)
Background: Heat waves are correlated with increased mortality in the aged population. Social isolation is known as a vulnerability factor. This study aims at evaluating the correlation between an intervention to reduce social isolation and the increase in mortality in the population over 80 during heat waves. Methods: The study adopts a retrospective ecologic design. We compared the excess mortality rate (EMR) in the over 80 population during heat waves in urban areas of Rome (Italy), where a program to reduce social isolation was implemented compared to others where it was not implemented. We measured mortality of the summer periods from 2015 to 2019 compared with 2014 (a year without heat waves). Winter mortality, cadastral income and proportion of over 90 were included in the multivariate Poisson regression. Results: The EMR in the intervention and controls was 2.70% and 3.81%, respectively. Rate ratio 0.70 (c.i. 0.54 - 0.92, p-value 0.01). The Incidence Rate Ratio (IRR) of the interventions with respect to the controls is 0.76 (c.i. 0.59 - 0.98). After adjusting for other variables, the IRR was 0.44 (c.i. 0.32 - 0.60). Conclusions: Reducing social isolation could limit the impact of heat waves on the mortality of the elderly population.
ARTICLE | doi:10.20944/preprints202207.0356.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Multi-site statistical downscaling; Generative Adversarial Network; Combination of Errors; Convolutional Neural Network; Struc-tural Similarity Index; Wasserstein GAN; extreme precipitation
Online: 25 July 2022 (07:59:59 CEST)
Although the statistical methods of downscaling climate data have progressed significantly, the development of high-resolution precipitation products continues to be a challenge. This is especially true when interest centres on downscaling value over several study sites. In this paper , we report a new downscaling method termed the multi-site Climate Generative Adversarial Network (MSCliGAN), which can simulate annual maximum precipitation to the regional scale during the 1950-2010 period in different cities in Canada by using different AOGCM's from the Coupled Model Inter-Comparison Project 6 (CMIP6) as input. Auxiliary information provided to the downscaling model included topography and land-cover. The downscaling framework uses a convolution encoder-decoder U-net network to create a generative network and a convolution encoder network to create a critic network. An adversarial training strategy is used to train the model. The critic/discriminator used Wasserstein distance as a loss measure and on the other hand the generator is optimized using a summation of content loss on Nash-Shutcliff Model Efficiency (NS), structural loss on structural similarity index (SSIM), and adversarial loss Wasserstein distance. Downscaling results show that downscaling AOGCMs by incorporating topography and land-use/land-cover can produce spatially coherent fields close to observation over multiple-sites. We believe the model has sufficient downscaling potential in data sparse regions where climate change information is often urgently needed.
ARTICLE | doi:10.20944/preprints202108.0150.v1
Subject: Earth Sciences, Atmospheric Science Keywords: rainfall trend; Mann Kendall’s test; Sen’s slope estimator; climate statistics; seasonal rainfall; standardized anomaly index; extreme precipitation indicators; rainfall variability; southern Ghana
Online: 6 August 2021 (08:01:09 CEST)
Rainfall variability has resulted in extreme events like devastating floods and droughts which is the main cause of human vulnerability to precipitation in West Africa. Attempts have been made by previous studies to understand rainfall variability over Ghana but these have mostly focused on the major rainy season of April-July, leaving a gap in our understanding of the variability in the September-November season which is a very important aspect of the Ghanaian climate system. The current study seeks to close this knowledge gap by employing statistical tools to quantify variabilities in rainfall amounts, rain days, and extreme precipitation indices in the minor rainfall season over Ghana. We find extremely high variability in rainfall with a Coefficient of variation (CV) between 25.3% and 70.8%, and moderate to high variability in rain days (CV=14.0% - 48.8%). Rainfall amount was found to be higher over the middle sector (262.7 mm – 400.2 mm) but lowest over the east coast (125.2 mm – 181.8 mm). Analysis of the second rainfall season using the Mankandell Test presents a non-significant trend of rainfall amount and extreme indices (R10, R20, R99p, and R99p) for many places in southern Ghana. Rainfall Anomaly Indices show that the middle sector recorded above normal precipitation which is the opposite for areas in the transition zone. The result of this work provides a good understanding of rainfall in the minor rainfall season and may be used for planning purposes.
ARTICLE | doi:10.20944/preprints202107.0301.v1
Subject: Engineering, Automotive Engineering Keywords: Deficit volume; drought intensity; drought magnitude; extreme number theorem; Markov chain; moving average smoothing; standardized hydrological index; sequent peak algorithm; reservoir volume.
Online: 13 July 2021 (11:25:59 CEST)
The traditional sequent peak algorithm (SPA) was used to assess the reservoir volume (VR) for comparison with deficit volume, DT, (subscript T representing the return period) obtained from the drought magnitude (DM) based method with draft level set at the mean annual flow on 15 rivers across Canada. At an annual scale, the SPA based estimates were found to be larger with an average of nearly 70% compared to DM based estimates. To ramp up DM based estimates to be in parity with SPA based values, the analysis was carried out through the counting and the analytical procedures involving only the annual SHI (standardized hydrological index, i.e. standardized values of annual flows) sequences. It was found that MA2 or MA3 (moving average of 2 or 3 consecutive values) of SHI sequences were required to match the counted values of DT to VR. Further, the inclusion of mean, as well as the variance of the drought intensity in the analytical procedure, with aforesaid smoothing led DT comparable to VR. The distinctive point in the DM based method is that no assumption is necessary such as the reservoir being full at the beginning of the analysis - as is the case with SPA.
ARTICLE | doi:10.20944/preprints202201.0398.v1
Subject: Physical Sciences, Atomic & Molecular Physics Keywords: B-spline R-matrix; R-matrix with time dependence; intense short-pulse extreme ultra14 violet radiation; time-dependent Schrdinger equation; Arnoldi-Lanczos propagation
Online: 26 January 2022 (13:01:07 CET)
Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation
ARTICLE | doi:10.20944/preprints202201.0107.v1
Subject: Engineering, Energy & Fuel Technology Keywords: Very short term load forecasting; VSTLF; Short term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; extreme gradient boosting, energy consumption; ARIMA; time series prediction.
Online: 10 January 2022 (12:17:35 CET)
Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.
CONCEPT PAPER | doi:10.20944/preprints202102.0228.v1
Subject: Materials Science, Biomaterials Keywords: energetic materials; solid propulsion systems; extreme thrust control; reaction zones; functionalized carbon-based nanostructured metamaterials; nano-sized additives; carbon atomic wires, sp1-hybridized bonds; ion-assisted pulsed-plasma deposition; self-organizing of the nanostructures; universal phenomena of nano-cymatics; electrostatic field; synergistic effect
Online: 9 February 2021 (09:48:42 CET)
A new generation of nano-technologies is expanding solid propulsion capabilities and increasing their relevance for versatile and manoeuvrable micro-satellites with safe high-performance propulsion. We propose the innovative concept, connected with application of new synergistic effect of the energetic materials performance enhancement and reaction zones programming for the next generation small satellite multimode solid propulsion system. The main idea of suggested concept is manipulating by the self-organized wave patterns excitation phenomenon, by the properties of the energetic materials reaction zones and by localization of the energy release areas. This synergistic effect can be provided through application of the functionalized carbon-based nanostructured metamaterials as a nano-additives along with simultaneous manipulating by their properties through the electrostatic field. Mentioned effect will be controlled through predictive programming both by the spatial structure and physics-chemical properties of the functionalized carbon-based nano-additives and through the electromagnetic control of the self-organized wave pattern excitation and micro- and nano- scale oscillatory networks in the energetic material reaction zones. Suggested new concept makes it possible to increase the energetic material regression rate and increase the thrust of the solid propulsion system with minimal additional energy consumption.