ARTICLE | doi:10.20944/preprints201803.0199.v1
Subject: Engineering, Civil Engineering Keywords: Reservoir operation; SWAT; Genetic Algorithm; Urbanisation; Ganga River
Online: 23 March 2018 (15:07:22 CET)
Reservoirs are recognized as one of the most efficient infrastructure components in integrated water resources management and development. At present, with the ongoing advancement of social economy and requirement of water, the water resources shortage problem has worsened, and the operation of reservoirs, in terms of consumption of flood water, has become significantly important. Reservoirs perform both regulation of flood and integrated water resources management, in which the flood limited water level is considered as the most important parameter for trade-off between regulation of flood and conservation. To achieve optimal operating policies for reservoirs, large numbers of simulation and optimization models have been developed in the course of recent decades, which vary notably in their applications and working. Since each model has their own limitations, the determination of fitting model for derivation of reservoir operating policies is challenging and most often there is always a scope for further improvement as the selection of model depends on availability of data. Subsequently, assessment and evaluation associated with the operation of reservoir stays conventional. In the present study, the Soil and Water Assessment Tool (SWAT) models and a Genetic Algorithm model has been developed and applied to two reservoirs in Ganga River basin, India to derive the optimal operational policies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. As a result, a simulation-based optimization model was recommended for optimal reservoir operation, such as allocation of water, flood regulation, hydropower generation, irrigation demands and navigation and e-flows using a definite combination of decision variables. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on simulated result, in the present case study it is concluded that GA-derived policies are promising and competitive and can be effectively used operation of the reservoir.
ARTICLE | doi:10.20944/preprints202111.0085.v1
Subject: Earth Sciences, Environmental Sciences Keywords: coast; erosion; urbanisation; airborne imagery; spaceborne imagery; French Polynesia
Online: 3 November 2021 (14:23:15 CET)
Coastal urbanisation is a widespread phenomenon throughout the world and is often linked to increased erosion. Small Pacific islands are not spared from this issue, which is of great importance in the context of climate change. The French Polynesian island of Bora Bora was used as a case study to investigate the historical evolution of its coastline classification and position from 1955 to 2019. A time series of very-high-resolution aerial imagery was processed to highlight the changes of the island’s coastline. The overall length of natural shores, including beaches, decreased by 46% from 1955 to 2019 while man-made shores such as seawalls increased by 476%, and as of 2019 represented 61% of the coastline. This evolution alters sedimentary processes: the time series of aerial images highlights increased erosion in the vicinity of seawalls and embankments, leading to the incremental need to construct additional walls. In addition, the gradual removal of natural shoreline types modifies landscapes and may negatively impact marine biodiversity. Through documenting coastal changes on Bora Bora through time, this study highlights the impacts of man-made structures on erosional processes and underscores the need for sustainable coastal management plans in French Polynesia.
ARTICLE | doi:10.20944/preprints202207.0248.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Landsat; urban growth; Land Use Land Cover (LULC); remote sensing; urbanisation; NDVI
Online: 18 July 2022 (04:49:07 CEST)
Land Use Land Cover (LULC) change and urban growth have a significant influence on local climate of cities. From 1985 to 2021 the population of Baghdad increased by 103%. Therefore, the risen question is how this expansion influences the temperature of the city. The study aims to identify urban growth of Baghdad, investigate its influence on variation of Land Surface Temperature (LST) and identify the main factors that control the surface temperature of the city. Three Landsat images from 1985 to 2021, in addition to sixteen potential factors, were used in the study. Our findings suggest that during the study period, vegetated areas declined by 39% while built-up class increased by 139%. Bare soil recorded the highest surface temperature. The study found that surface temperature has a strong inverse relationship with vegetation (Normalized Difference Vegetation Index (NDVI): r = -0.62, p < 0.001) and moisture (Normalized Difference Moisture Index (NDMI): r = -0.65, p < 0.001). Therefore, increasing vegetation and water body lead to decrease temperature of the city. Our findings help policymakers to deal with climatic issues rising from urban growth of the city.
ARTICLE | doi:10.20944/preprints202010.0279.v1
Subject: Engineering, Automotive Engineering Keywords: Covid-19; Resilience; Sustainable Development Goals; Technology; Urbanisation; Climate Change; Complex Systems; Systemic Change; Future of Sustainable Development
Online: 13 October 2020 (12:18:09 CEST)
Washing hands, social distancing and staying at home are the preventive measures set in place to contain the spread of the COVID-19, a disease caused by SARS-CoV-2. These measures, although straightforward to follow, highlight the tip of an imbalanced socio-economic and socio-technological iceberg. Here, a System Dynamic (SD) model of COVID-19 preventive measures and their correlation with the 17 Sustainable Development Goals (SDGs) is presented. The result demonstrates a better informed view of the COVID-19 vulnerability landscape. This novel qualitative approach refreshes debates on the future of SDGS amid the crisis and provides a powerful mental representation for decision makers to find leverage points that aid in preventing long-term disruptive impacts of this health crisis on people, planet and economy. There is a need for further tailor-made and real-time qualitative and quantitative scientific research to calibrate the criticality of meeting the SDGS targets in different countries according to ongoing lessons learned from this health crisis.
ARTICLE | doi:10.20944/preprints201809.0219.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: informal settlement indicators; very high resolution (VHR); urbanisation; sustainable development goals; object-based image analysis (OBIA); machine learning (ML); random forest (RF)
Online: 12 September 2018 (12:32:25 CEST)
The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.