Mapping Socio-Economic Vulnerability to Natural Hazard in Urban Areas Using Machine Learning and Indicator-Based Approaches

1. Introduction

Urbanization and climate change are significantly impacting human health, socio-economic stability, and sustainability. Urbanization, the process resulting from the increase in urban residents and expansion of the built-up areas, is often driven by economic opportunities and improved living standards, including access to employment, education, and healthcare [1]. However, many urban residents, particularly in the Global South, face critical challenges, such as inadequate housing, contaminated water, insufficient sanitation, and poor waste management [2]. The rapid urbanization has outpaced the capacity of urban planning processes to effectively address these challenges, exacerbating risks faced by urban residents [3]. With the frequent occurrence of natural hazards induced by climate change, rapid urbanization exacerbates various risks, which lead to adverse outcomes from exposure to natural hazards plus the vulnerabilities inherent in the affected urban population [4]. In this context, risk is understood as an outcome of the interaction between natural hazard, exposure, and vulnerability [5]. Therefore, extreme natural physical events that result in loss of lives, injuries, or other health effects, as well as harm to property, infrastructure, livelihoods, and ecosystems, are referred to as natural hazards [6]. For instance, because of their physical positions and the concentration of human activity, urban areas are vulnerable to hazards like storms, floods, and cyclones. The consequences of such hazards can be devastating, leading to loss of lives and economic disruptions in essential services. Exposure to hazards describes the degree to which urban residents are subjected to these hazardous events [5]. In rapidly growing cities, factors such as high population density associated with inadequate infrastructure and informal settlements often make their residents vulnerable, particularly during extreme weather events [7].

Vulnerability in this study is defined as the predisposition to suffer adverse effects from exposure to natural hazards, as expressed by the Intergovernmental Panel on Climate Change (IPCC) [8]. Vulnerability encompasses several dimensions, including physical, social, economic, and attitudinal vulnerability [9]. Physical vulnerability refers to the direct harm, like injuries or fatalities, caused by hazards such as extreme weather [9]. Social vulnerability refers to the increased risk from natural hazards due to factors like demographic characteristics (age, gender, level of education), community cohesion, and resource access [10]. The economic vulnerability involves the financial losses from hazards and the resources available for recovery, while attitudinal vulnerability highlights how attitudes and behaviors can affect preparedness and response [11,12]. In this study, the primary focus is on social and economic vulnerability, as these dimensions are critical for understanding how different individuals or groups experience and cope with natural hazards. The choice of these dimensions is influenced by the fact that vulnerability is also understood further through three interrelated components: sensitivity, susceptibility, and adaptive capacity [5,13]. Sensitivity reflects how severely a community or individual is affected by changes brought about by hazards [8]. For instance, populations with limited access to healthcare facilities and services may suffer more profoundly during health crises or natural disasters [14,15]. Susceptibility denotes inherent weaknesses that predispose communities or individuals to adverse effects; for example, low-income groups or poorest communities may lack resources or social support systems that could mitigate harm during emergencies [8]. Adaptive capacity is a critical aspect of vulnerability that describes the ability of communities or individuals to adjust in response to harmful events or mitigate potential damage [8,16]. This capacity is influenced by various factors, including socio-economic status [7]. In this regard, urban areas with strong socio-economic status are frequently better able to respond to and adapt to hazards.

Urban socio-economically deprived individuals and communities often reside in areas prone to natural hazards, which significantly heightens their exposure to various risks [7]. This exposure is especially hazardous because these populations often possess limited capacity for mitigation or adaptation, rendering them highly vulnerable to natural hazards [16]. Natural hazards occurrence is becoming more frequent and more severe as urbanization and climate change continue to grow. This occurrence poses a severe threat to public health, particularly in Global South areas with high socio-economic sensitivity and poor adaptive capacity [17]. Understanding socio-economic vulnerability associated with the climate change related hazards is crucial for developing effective strategies aimed at preventing risks and mitigating damages caused by natural hazards toward enhanced socio-economic conditions and public health outcomes [7,10]. However, this understanding is missing due to the high reliance on extensive data that is often unavailable in many cities of the Global South. Consequently, the assessment of socio-economic vulnerability to natural hazards is frequently ignored despite its importance to identify how natural hazards affect the urban population [7,18].

Countries in the Global South lack the required data for assessing socio-economic vulnerability to natural hazards, which complicates their ability to implement effective disaster risk reduction strategies [19]. One of the primary issues is the lack of comprehensive and reliable data, which is critical for understanding the socio-economic conditions of vulnerable populations [20]. Many areas do not have systematic data collection mechanisms, leading to gaps in information regarding population demographics, income levels, health outcomes, and access to basic services [19]. This absence of data hinders the development of accurate vulnerability assessments and makes it difficult for policymakers to identify the most at-risk communities and tailor interventions accordingly [21]. Moreover, the quality of available data is often questionable. In many cases, existing data are outdated or incomplete, failing to capture the changing socio-economic conditions. For instance, socio-economic data of informal settlement dwellers are often not included in national statistics, leading to an underestimation of the risks faced by their residents [21]. In this regard, reliance on national-level data obscures local vulnerabilities, as small spatial scale disparities in socio-economic conditions and exposure to hazards are not adequately represented at large spatial scales. Additionally, socio-political factors further complicate the data collection efforts. In many Global South countries, political instability, corruption, and inadequate governance can impede effective data gathering and sharing [22]. Furthermore, the data challenge results from a lack of political will to prioritize socio-economic vulnerability in risk assessment and management or a lack of budget for data collection initiatives [22]. As natural hazards become more prevalent in urban areas, reliable data is needed to support risk assessments and adaptation strategies.

Recent advancements in technology offer potential solutions to data challenges. Remote sensing and machine learning techniques have shown a promising capacity to obtain and process large-scale datasets for hazard modeling. Several studies have applied these technologies to address various urban challenges such as flooding, landslides, and gullies development in urban areas. For instance, [7,23,24] have use remote sensing-based satellite imagery in combination with machine learning for measuring flood susceptibility. Refs. [25,26,27,28] applied machine learning to evaluate multi-hazards including flooding and landslides. Despite these advancements, most of the studies utilizing remote sensing and machine learning have primarily focused on hazard assessment rather than integrating socio-economic vulnerability into their risk frameworks. While few studies have attempted this integration ([12,13,29,30]), they often develop approaches and frameworks tailored to specific input data sets and localized areas, which limits their broader applicability in both data-rich and data-scarce regions. This underscores the need for new approaches that can bridge the gap between hazard modeling and socio-economic vulnerability assessment.

Therefore, this study proposed a flexible and scalable framework for mapping socio-economic vulnerability to natural hazards in urban areas, specifically designed to support more effective and equitable public health interventions in data-scarce urban environments. The significance of this research lies in its potential to address gaps in understanding how socio-economic factors influence vulnerability to natural hazards, particularly in rapidly urbanizing regions of the Global South. By focusing on the City of Kigali, the proposed framework was applied to map flood susceptibility and socio-economic vulnerability to flooding. The obtained maps were locally validated through a combination of historical flood data comparison, comparative analysis with existing socio-economic studies, adherence to established methodologies, and qualitative assessments using local knowledge. In order to evaluate the scalability of the proposed framework and enable comparative analysis across several urban contexts, it was applied to the cities of Dar es Salaam, and Kampala, in Tanzania and Uganda, respectively. The results derived from this study contribute to a deeper understanding of socio-economic vulnerabilities in urban areas prone to natural hazards, ultimately informing policy decisions and resource allocation for targeted interventions for enhanced resilience and improved public health outcomes.

This paper is structured in the following manner: materials and methods for this study are presented in the next part, which also gives a summary of the proposed framework and how it was utilized to map Kigali's socioeconomic susceptibility to floods. This is followed by a section on the scalability and transferability of the proposed framework, which highlights its relevance in different urban settings. The results and discussion section present key findings and their implications, and limitations of the study. Finally, a concluding section summarizes the results and emphasizes the importance of the study and integration of socio-economic vulnerability assessments into urban planning and disaster risk management strategies.

2. Materials and Methods

2.1. Description of the Proposed Framework

The proposed framework is a result of a review of recent research on hazard risk modeling and mapping and socio-economic vulnerability assessment [7,12,18,24,28,30–34]. As seen in Figure 1, the framework is composed of three primary components. The first component, which is shown in blue on the left side of Figure 1, consists of measuring hazard susceptibility by combining machine learning models with data from remote sensing. This component enables users to model susceptibility for one or multiple hazards by leveraging data that is readily available for the area of interest. The flexibility inherent in this component allows practitioners to select the most suitable machine learning algorithms depending on the specific characteristics of geographical area, the hazards being modeled, and available data. Various machine learning methods, including Random Forest (RF), Gradient-Boosted Decision Trees (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), have demonstrated good performance in hazard susceptibility assessments in various studies [23,24,35–38]. The application of these models facilitates an understanding of hazard dynamics.

The second component, presented in green on the Figure 1’s right side, involves analyzing socio-economic data to evaluate socio-economic vulnerability. This component emphasizes the use of diverse socio-economic indicators and multivariate analysis techniques, such as regression models, Principal Component Analysis (PCA), Analytic Hierarchy Process (AHP), and Composite Indicator (CI) approaches [10,33,39]. These methodologies enable to quantify sensitivity and adaptive capacity effectively. The final component, represented at the bottom in orange, creates a socio-economic vulnerability index by combining hazard susceptibility with quantified sensitivity and adaptive capacity. This combination is crucial for assessing and mapping socio-economic vulnerability associated with natural hazards, as demonstrated by various studies [7,9,13,40]. Therefore, the proposed framework would support the development of effective hazard management strategies and inform policy decisions aimed at enhancing community resilience.

2.2. Application of the Proposed Framework to Mapping Socio-Economic Vulnerability to Flooding in the City of Kigali

The case study of the City of Kigali, Rwanda, where flooding is a frequent natural hazard, was used to evaluate the proposed framework. The following sections present the case study area, a description of the data used and their sources, historical flooding data, data about factors influencing floods, and socio-economic data. These are followed by the estimation of flood susceptibility by training and testing various machine learning models. The best-performing model was used to obtain the flood susceptibility index. Additionally, the study applied Indicator-based approaches to compute Sensitivity and Adaptive Capacity using socio-economic data. The obtained result was finally used to compute and map socio-economic vulnerability to flooding by integrating flood susceptibility. Details on each step are presented in the following sections.

2.2.1. Description of City of Kigali

The City of Kigali is the largest city of Rwanda, and serves as the capital and focal point of economic activities. In terms of administration, the City of Kigali is comprised of three districts (Figure 1), which are further subdivided into 35 sectors, which account for 161 cells and 1,176 villages. The village represents the lowest and smallest administrative unit, which is referred to neighborhood in this study [41]. Located near the geographic center of the country, Kigali is a city that is exhibiting rapid urban growth and economic transformation. Spanning over 730 square kilometers, it is home to more than 1.7 million people and is pivotal to Rwanda's socio-economic landscape [41]. The city exhibits a diverse array of land-use types, including commercial, residential, industrial, agricultural, and public facilities, alongside wetlands and water bodies [42]. Over the past two decades, Kigali has undergone significant urban expansion and development. The city has seen a rapid increase in built-up areas whereby the urban landscape is a mosaic of modern high-rise buildings, residential neighborhoods, commercial zones, and informal settlements [43,44]. This is particularly true in the urban central core, where continuous modernization is observed through the presence of modern buildings and upgraded road networks. This rapid urbanization has often outpaced the development of adequate infrastructure [45]. Consequently, majority of informal settlements are frequently found in the most vulnerable areas, which are highlighted by Kigali’s urban fabric and include steep hillsides and flood-prone valleys [42]. These areas lack proper sanitation, drainage, and other basic services, exacerbating the vulnerability of their residents to natural hazards [46]. Despite significant economic progress, substantial socio-economic inequalities persist, with large segments of the population living in poverty [43,47]. Many residents, particularly those living in informal settlements, do not have access to essential services including sanitation facilities, clean water, and healthcare [46,48]. These socio-economic disparities mean that the poorest and most vulnerable populations are disproportionately affected by natural hazards. They often reside in the most at-risk areas and have the least capacity to recover from adverse events, creating a cycle of vulnerability and poor health outcomes.

Kigali's geography is characterized by its hills and valleys, with elevations ranging from approximately 1,300 meters to over 1,600 meters above sea level, which influences the city's drainage patterns [42]. The steep slopes accelerate water flows, resulting in a higher volume of water that accumulates in valleys, which, when combined with seasonal severe rainfalls, frequently leads to flooding [49]. Flooding in Kigali, like in other areas, is a complex natural hazard characterized by the overflow of water beyond its normal limits, often resulting from heavy and prolonged rainfall [50]. Flooding is particularly noticeable in urban areas due to rapid urban growth, which has reduced vegetation cover and increased impervious surfaces and runoff, and hence exacerbating the risk of flooding in places with lower elevation. Furthermore, the combination of increased surface runoff due to urbanization and inadequate drainage systems hinders groundwater recharge and leads to the accumulation of excess water during rain events [51].

The consequences of flooding in Kigali, like in other urban areas, are severe and multifaceted. Immediate impacts include loss of life and destruction of buildings, utilities, roads, bridges, and other infrastructure. Additionally, flooding can compromise water supply systems, increasing exposure to contaminated water and facilitating the spread of infectious diseases such as dengue, malaria, measles, meningitis, and typhoid [52]. Furthermore, flooding impacts individuals with chronic health conditions by damaging critical infrastructure and creating barriers to accessing essential health services [14,15].

2.2.2. Overview of Data

The study used a data-driven approach following the proposed framework to map socio-economic vulnerability to flooding for public health interventions in Kigali. In the City of Kigali, historical flood data has not been recorded as geospatial data, making it challenging to extract geospatial flood information directly from reports. To overcome this, data on previous floods was extracted using Synthetic Aperture Radar (SAR) imagery from Sentinel-1 using the same methods as studies for flood modeling [53,54]. Based on flood incidents reported by the Rwandan Ministry in Charge of Emergency Management, two SAR images were selected: one acquired prior to a period of heavy rainfall on December 22, 2019, and the other taken following a flood event on December 25, 2019. Image ratioing and Otsu's thresholding methods [55] were applied to detected and delineate floodwater, from which 456 flood points and 484 non-flood points were randomly generated (A figure illustrating their distribution is a presented in Figure A1 in Appendix A). These were divided into 80% training and 20% testing datasets. Non-flooded points were assigned a value of 0, whereas the target class value points were given a value of 1. In addition, nine flood-influencing factors were identified after a review of various studies on flood susceptibility assessment and considering the geographic appearances of the study area. Table 1 describes the factors that influence floods and the associated data sources used in this study.

The min-max approach was used to normalize all factors to the range [0,1] after they were transformed to raster with a spatial resolution of 10m (Equation 1).

$$X_{norm}=\frac{X-X_{min}}{X_{max}-X_{min}}$$

Where X is the original value, X_norm is the normalized value, X_min is the dataset's minimal value, and X_max is its highest value. This provides data ranging from 0 as low value and 1 as maximum value for all factors except for land cover, whereby low values represent class water class followed by forest, green spaces, and agricultural land toward built-up and bare land classes. Normalization is used in data analysis as a critical preprocessing step because the study used various datasets containing variables that were measured on different scales. Therefore, by applying normalization, all variables in datasets were transformed to a common scale to ensure that no single variable disproportionately influences the results due to its scale [67]. In addition, a correlation analysis was used to detect multicollinearity among these factors (Figure B2 in the Appendix B illustrates the correlation matrix for all factors). The goal of this correlation analysis was to identify and remove highly correlated factors that could adversely affect model performance and interpretation, leading to unreliable predictions [68]. Multicollinearity was quantified by correlation coefficient, which provides insight into the relationships between input factors. A correlation coefficient greater than 0.7 typically indicates a strong correlation, suggesting potential multicollinearity issues. Thus, any factor exceeding a correlation coefficient of 0.7 was not included. Figure 3 presents factors that were included in susceptibility modeling. The results on the correlation analysis are presented in Appendix B.

2.2.3. Flood Susceptibility Estimation with Machine Learning Models

Machine Learning models like Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), RF, SVM and XGBoost and ANN, have been widely used for analyzing and assessing natural hazards risks [25–28,30,69]. All models present advantages as well as disadvantages, and no single model is known to be the best generalized model for the assessment and analysis of hazard risks. Four machine learning models—RF, SVM, XGBoost, and Multilayer Perceptron (MLP)—were employed in this study. The choice of these models was guided by existing literature on modeling hazard risks, and which highlighted their ability to combine the raster images of environmental factors influencing flood occurrence [24]. These models are suitable for mapping flood susceptibility because they are able to learn complex and non-linear associations between various input data spatial data. Additionally, these models were selected based on the datasets that were locally available for the study. Accuracy, Precision, Recall, F1-Score, and the area under the receiver operating characteristic curve (AUC) were used to assess the performance of models. These metrics are widely used in machine learning to assess the performance of models, especially in classification tasks [24,25,30,35,68]. Each model was optimized using k-fold cross-validation and hyperparameter tuning. Consequently, flood susceptibility indices were obtained using the model that performed the best based on the evaluation metrics on the testing dataset. The obtained indices were used to create a map of flood susceptibility index, whereby for comprehensive visualization, indices obtained were grouped in intervals for ease based on the natural breaks classifying method. In addition, flood susceptibility was aggregated at neighborhood-level. This aggregation facilitates understanding of susceptibility at small administrative level, which help the framework to provide more actionable information required for local authorities and stakeholders to formulate targeted interventions [70].

2.2.4. Mapping Socio-Economic Vulnerability to Flood

Mapping of socio-economic vulnerability to natural hazards consisted of the selection of socio-economic factors, data collection, data analysis/construction socio-economic vulnerability index/score, and socio-economic vulnerability mapping. Socio-economic indicators employed in this study were selected after a review of literature and available data sets. The indicators included are described in Table 2.

Following the selection of indicators and the collection of data for each indicator, the values were normalized using the Min-Max method (see equation 1), which results in values between 0 and 1, with 0 denoting the lowest value and high denoting the highest value for each indicator. This allowed for the generation of comparable datasets. Figure 4 illustrates these normalized values and their geographic implications, providing a clear depiction of how various socio-economic factors contribute to overall vulnerability levels.

To construct a socio-economic vulnerability score, the study initially tried PCA, which is known as the best for avoiding expert bias while speeding up the process of the assessment [13]. To ensure that the available datasets were suitable for PCA, the study conducted the Kaiser-Meyer-Olkin (KMO) test. The results indicated that the datasets were not suitable for PCA whereby KMO was below 0.8, a threshold for which the PCA is considered reliable [78]. Benefitting from the flexibility of the proposed framework, the study employed an indicator-based approach to compute the socio-economic vulnerability index as an alternative. The overall socio-economic vulnerability (SEVi) was calculated by combining flood susceptibility (FSi), sensitivity (Si), and adaptive capacity (ACi) into a unified index, following equation (2) adopted from [12]:

SEV_i = FS_i + S_i - AC_i

(2)

Whereby sensitivity S_i for each area i was calculated as the sum of the proportions of Pop_d, Pop_<5, and Pop_>65 represent the normalized proportions of the population density, population under 5 years and over 65 years in each area, respectively, given by equation (3) adopted from [10]:

S_i = Pop_d + Pop_<5 + Pop_>65

(3)

Adaptive capacity AC_i was determined by access to key infrastructure and services that support community resilience to environmental hazards. Therefore, the adaptive capacity index was calculated by following equation (4):

AC_i = PHF_i + POI_i + RN_i

(4)

Where PHF_i, POI_i, and RN_i are the normalized values representing access to healthcare, the density of POIs, and road network infrastructure in area i. Obtained SEV_i was normalized using equation (1). Since flood susceptibility was aggregated at neighborhood level, socio-economic vulnerability was also aggregated at the same scale to keep spatial consistency, allow understanding of spatial distribution of vulnerability at small administrative unity for facilitating communication of information.

2.2.5. Validation of Flood Susceptibility and Socio-Economic Vulnerability Maps

The flood susceptibility map was validated against historical flood data by assessing the correspondence between identified susceptible areas and actual past flood events, as illustrated in Figure C1 of Appendix C. Additionally, the socio-economic vulnerability map was validated through comparative analysis with existing studies that have mapped socio-economic inequalities and poverty within the same study area. This validation process was further strengthened by referencing methodologies from other successful flood susceptibility modeling and socio-economic vulnerability assessments, ensuring adherence to established standards. Furthermore, qualitative validation was achieved through visual inspections and local knowledge, which helped confirm that the outputs were consistent with the area's social, economic, geographic, and environmental conditions.

2.3. Scalability and Transferability of the Framework

To evaluate whether the proposed framework is transferable, the study focused on historical flooding data from Kampala and Dar es Salaam, which were extracted from Sentinel-1 imagery, specifically targeting flood events that occurred between May 2019 and September 2020 in Kampala and October 2020 in Dar es Salaam (https://floodlist.com/africa, accessed on July 2024). To facilitate this analysis, Digital DEMs from the Shuttle Radar Topography Mission (SRTM) were utilized to derive essential topographic features, including slope, elevation, aspect, and drainage density. Furthermore, cumulative rainfall data were sourced from CHIRP, while land cover information was obtained from ESRI. The NDVI and NDBI were calculated using Sentinel-2 images. The scalability and transferability were limited to testing machine learning models and tuning and validation to ensure accurate predictions.

Initially, the MLP model trained on data from Kigali was applied to predict flooding in both Kampala and Dar es Salaam. This step aimed to evaluate how the model can adapt to different geographical contexts. Following this initial application, the model trained on Kigali was fine-tuned by using subsets of data specific to Kampala and Dar es Salaam. This iterative process was aimed at learning how model capability improves while being exposed to local conditions through data variation for flood susceptibility mapping. Furthermore, the scalability of the proposed framework was evaluated by training MLP, SVM, RF, and XGBoost models in both cities, systematically splitting the available data into training (80%) and testing (20%) sets. This methodological approach allows for a comprehensive analysis of how effectively the framework can be adapted and applied across different urban contexts, even when the transferability is not well-suitable.

4. Results and Discussion

4.1. Flood Susceptibility Map

The results presented in Table 3 indicate the performance of the model based on AUC, Accuracy, Precision, Recall, and F1-Score metrics. Figure 5 presents variation of AUC on test data for all models. The results show that the MLP model exhibits the best performance with an AUC of 0.902, indicating it has the highest ability to distinguish between positive and negative cases. This performance is complemented by its Accuracy of 0.85, meaning it accurately predicts 85% of cases. MLP also excels with a Precision score of 0.83. MLP also presents a high Recall of 0.90, indicating they are equally effective at capturing true positive cases. Finally, the MLP again outperforms others with an F1 Score of 0.86, reflecting its overall effectiveness in balancing precision and recall. Following closely, the SVM model demonstrates nearly equivalent performance to MLP, though it has slightly higher prediction errors. The RF model performs slightly behind SVM in terms of AUC but still shows excellent classification ability, as witnessed by its metrics. Finally, the XGBoost model is marginally less effective than the other models, though its performance is still commendable. While all models show strong performance with minimal differences, making them all viable candidates for classification tasks, MLP stood out slightly in all metrics. Thus, it was selected as the best model and was applied to the entire study area to compute flood susceptibility.

While MLP and SVM demonstrate high performance, they are less interpretable and require additional methods to understand the factors that significantly contribute to their predictive capabilities. MLPs, like other artificial neural networks, are often regarded as black box models due to their complex architectures, which offer minimal inherent interpretability [79]. Similarly, the non-linear kernel SVM model used in this study also lacks transparency and interpretability [80]. This makes their adoption to the domain application for decision makers very hard. In contrast, RF and XGBoost emerge as more interpretable models. Both models have a straightforward analysis of feature importance, allowing users to easily identify which features most significantly influence predictions. Figure 6(a) and 6(b) illustrate the feature importance for RF and XGBoost, respectively. The results shown in these figures indicate that slope and elevation play crucial roles in predicting flood susceptibility across the City of Kigali.

Figure 7 also indicates partial dependence plots for slope and elevation for RF model. The results in the figure illustrates that for the slope, the predicted probability of the flooding is high for lower slope values but decreases sharply as the slope exceeds approximately 0.1, eventually stabilizing at higher values. While for Elevation, the figure indicates a strong negative relationship with the predicted outcome, where the probability is high at very low elevations but decreases substantially as elevation increases, remaining constant at higher elevation levels.

The result presented in Figure 8 (a) and (b) present flood susceptibility map generated using MLP model, and its respective aggregation at the neighborhood level. These results highlight part of the city with darker brown shades, which represent higher susceptibility to flooding, while lighter shades indicate lower susceptibility. The central and southern parts of the region show the highest susceptibility, which makes them more prone to flooding. On the contrary, the northern and northeastern parts show lower flood susceptibility. This result illustrates that the MLP model was able to identify areas with high flood susceptibility, which closely aligns with historically flooded locations. This model's strong predictive capabilities are rooted in its ability to map complex non-linear relationships between environmental factors such as topography, land use, and hydrological conditions, as highlighted by [25,27,28]. By integrating diverse data inputs such as topography, land use, and hydrological conditions, the model generates a comprehensive susceptibility map, which not only identifies high-risk zones but also provides critical insights for developing further actions targeted to protect the public against health risks associated with flood exposure. This ability of the model to predict flood susceptibility would allow decision-makers to enhance disaster preparedness, mitigate health risks, and implement community-level responses. Thus, the result emphasizes the need for targeted public health actions, as floods can significantly impact both physical infrastructure and public health by increasing the risk of waterborne diseases, injuries, and disruptions to healthcare access, as shown by malaria [14,81]. The resulting map would serve as a valuable tool for planners and public health officials, helping them prioritize flood prevention measures such as enhanced drainage systems, flood barriers, and land use regulations in high-risk areas while also guiding emergency preparedness and healthcare resource allocation.

4.2. Socio-Economic Vulnerability Map

The result presented in Figure 9 (a) and (b) present socio-economic vulnerability to flooding across the City of Kigali. They reveal how flood susceptibility, a prevalent environmental hazard in Kigali due to its hilly terrain and frequent heavy rainfall, disproportionately affects socio-economically vulnerable populations. The darker shaded areas on the map represent higher socio-economic vulnerability resulting from high flood susceptibility and sensitivity and with relatively low adaptive capacity. These areas are characterized by informal settlements, lower income levels, inadequate infrastructure, and limited access to essential services such as access to healthcare facilities, as highlighted [46,48]. In contrast, the central parts of Kigali, which exhibit lower vulnerability, benefit from improved urban planning, robust infrastructure, and more resilient housing, as shown by [43,47,49]. The spatial variation of socio-economic vulnerability across the city indicates that the presence a higher concentration of the population, especially young and elderly residents, reside in high-socio-economic vulnerable areas prone to flooding, such as next to wetlands or in informal settlements with poor drainage systems. These locations amplify the risks associated with heavy rainfall and flooding, potentially leading to catastrophic outcomes like property loss, displacement, and increased exposure to health hazards.

Socio-economic vulnerability to flooding highlights the limited capacity to respond and recover from flooding and often leads to widespread health problems, including outbreaks of waterborne diseases such as cholera, typhoid, and dysentery [7,13]. Floodwaters can contaminate drinking water supplies and overwhelm already fragile sanitation systems, creating a favorable environment for disease transmission [51]. Moreover, stagnant water resulting from poor drainage systems can become a breeding ground for mosquitoes, increasing the incidence of vector-borne diseases like malaria [52]. Thus, in urban populations with less adaptive capacity, such as residents with limited socio-economic means and limited access to healthcare access, these health issues can escalate rapidly, with a lack of timely medical intervention exacerbating the situation [14,15]. To address these issues, public health and urban planning policy can benefit from highlighting socio-economic vulnerability to flood hazard. The solutions can be achieved by focusing on targeted interventions that combine socio-economic upliftment with flood mitigation. Public health strategies should aim to improve access to healthcare in vulnerable areas, particularly by equipping health facilities to respond to flood-related health crises. Urban planning policy should ensure more equitable urban development initiatives that integrate socio-economic and environmental data to reduce vulnerability to flooding across the city. This includes ensuring that infrastructure investments are distributed more evenly, focusing particularly on informal settlements and other highly vulnerable areas. By addressing the root causes of socio-economic vulnerability to flooding, such as poverty and lack of access to services, the city can mitigate the public health impacts of flood hazards like flooding, which not only enhances the city's resilience to climate change but also contributes to long-term improvements in public health outcomes.

4.3. Scalability and Transferability

First, the study applied models trained on Kigali data to make predictions in Kampala and Dar es Salaam; the AUC and MAE obtained are presented in Table 4. In Kampala, the XGBoost model achieved the highest AUC of 0.519 and a relatively low MAE of 0.484, whereas in Dar es Salaam, the MLP model had the highest AUC at 0.402 and an MAE of 0.523. This indicates that the models trained on the Kigali dataset struggled to generalize across different urban settings, highlighting the need for continuous refinement of machine learning algorithms by incorporating local data and expert knowledge into model training processes. As urban environments evolve and data becomes more accessible, leveraging this information can lead to improved predictive Accuracy for flood events, ultimately contributing to better urban planning and flood disaster management strategies.

Then we fine-tuned network weights of MLP, which performed well on Kigali using small sub-sets of data from target cities (using 10%, 20%, 30%, 40%, and 50% of all available data for each city), performances reduced slightly to AUC of 0.491 and MAE of 0.520 for Kampala but improved slightly to AUC of 0.590 and MAE of 0.491 for Dar es Salaam. This lower performance of the employed models highlights potential limitations in their transferability, which can be due to the fact that cities are different in morphology and do not exhibit similar geographic/topographic characteristics. However, environmental conditions and urban dynamics differ significantly between cities [20,82]. For instance, all the cities used in this study to test the proposed framework exhibit different topographic/geographic patterns, socio-economic conditions, and climate. Thus, differences in the spatial distribution of flood hazard, infrastructure, or population density could affect model performance. Thus, the success of transferring models across cities may be constrained by variations in data availability, quality, or granularity.

However, despite the difficulties of transferability of the machine learning model trained on Kigali to Kampala and Dar es Salaam, the framework itself demonstrated scalability whereby when we trained the machine learning models using data from each respective city, the results on the test set revealed better performance of the models as shown in figure 10 (a and b). Additionally, the framework is designed to incorporate various data sources and machine learning models, enabling it to be applied in diverse urban environments. Its architecture allows for the integration of city-specific data, which shows its adaptability to different urban contexts.

4.4. Limitations of the Study

The study assessed and mapped social-economic vulnerability related to flood hazards using an assessment framework that leverages machine learning and indicator-based approaches. The study used different spatial data that depict different spatial aspects of flood susceptibility and urban socio-economic features. The results provide more targeted and accurate reference information for flood hazard adaptation and mitigation strategies. However, it should be noted that there are still limitations to its application: some additional variables associated with socio-economic vulnerability, such as education and income, were not included in this study since they were not available at a small scale in the study area. Although the above limitation exists in the current work, the proposed framework is designed to be flexible to allow application to areas with limited data. However, the availability of more data would provide more valuable outputs for supporting decision-making for planners and managers to positively deal with hazardous urban environments.

5. Conclusions

The effect of environmental hazards in urban areas has posed highly vulnerable situations for urban dwellers, especially dwellers with deprived socio-economic conditions. The vulnerability is highly accelerated by climate change, resulting in frequent extreme environmental hazards such as flooding, urban heat stress, and pollution. In this study, we proposed a scalable and transferable framework for mapping socio-economic vulnerability to urban environmental hazards in a data scarce urban environment. The framework integrates demographic and socio-economic factors, which are linked to remote sensing based urban environmental hazard factors using machine learning and spatial analysis. The framework was tested to map socio-economic vulnerability to flooding in the City of Kigali. Moreover, the scalability and transferability of the proposed framework were tested in Kampala, Uganda, and Dar es Salaam, Tanzania.

The results of this study indicated that the central, south, and west parts of Kigali are highly susceptible to flooding events. However, the central part is subject to the less socio-economic vulnerability associated with flooding hazards based on their enhanced socio-economic conditions, which enable dwellers of this area to adapt to the hazard event. However, areas with less socio-economic conditions are highly affected by flooding hazards. The results also indicate that the framework can be used to feature several hazards, allowing the assessment of socio-economic vulnerability related to multi-hazards in urban areas. Though the model trained in Kigali did not demonstrate efficient results when transferred to Kampala and Dar es Salaam, tuning the model with data portions from these respective cities improved efficiency, demonstrating the possibility of fine-tuning models toward transferability. The results of this study demonstrated the potential of employing multi-sourced, freely accessible data for mapping socio-economic vulnerability to environmental hazards in urban settings, which is essential to control vulnerable populations and establish strategic planning toward having more livable cities. Moreover, strengthening the ability of medical service supply and reducing inequality is also an effective vulnerability reduction strategy.