Mapping flood resilience: a comprehensive geospatial insight into regional vulnerabilities

Assam is highly susceptible to frequent flooding, which significantly affects socio-economic conditions. Despite the widespread application of Geographic Information System (GIS) based Analytical Hierarchy Process (AHP) models for flood susceptibility assessment, there remains a lack of micro-spatial analyses focused on semi-urban and rural fringe regions such as Dimoria Block. Existing regional-scale assessments often overlook local variations in topography, land cover, and hydrological conditions, limiting their applicability for site-specific planning. The present study integrates GIS and AHP to delineate flood susceptibility zones in Dimoria Block using eleven flood-conditioning parameters. Multicollinearity analysis was conducted, followed by weighted overlay and sensitivity analyses. Rainfall and land use/land cover (LULC) emerged as the most influential factors. The results indicate that 54.75% of the study area falls within the high flood susceptibility zone, 36.72% within the moderate zone, and 8.52% within the low zone. The final flood susceptibility map was validated using both quantitative and qualitative approaches. The receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) value of 0.936, indicating high predictive accuracy. Validation using field surveys and official flood records further supports the robustness of the model. This study presents a localized flood susceptibility assessment framework for Dimoria Block and demonstrates the applicability of GIS–AHP approaches in data-scarce environments. The findings provide useful inputs for flood mitigation planning, land-use management, and disaster risk reduction strategies at the local level.

Urban flooding is prevalent in low-lying, coastal regions, where subtle topographic variation, shallow groundwater, and impervious surfaces govern inundation dynamics. This study evaluates urban flood susceptibility across Miami-Dade County by integrating flood-conditioning factors, including elevation, slope, rainfall, land use/land cover, distance to roads and open water, stream power index (SPI), topographic wetness index (TWI), groundwater depth, and flow accumulation within an Analytical Hierarchy Process (AHP)-based weighted overlay framework. The AHP-derived weights demonstrated strong consistency (consistency ratio = 0.022) and were applied to reclassify each conditioning factor into five flood susceptibility classes—very low to very high. The model performance was evaluated using the Federal Emergency Management Agency (FEMA) flood zone, and the findings demonstrated that the AHP-based framework effectively differentiates flood susceptibility at a fine urban scale, achieving strong predictive performance; area under the Curve (AUC) = 0.85. The results also reveal pronounced spatial variability in flood susceptibility, with northeastern urbanized areas, particularly in Hialeah, Miami Gardens, Miami Lakes, and Downtown Miami, exhibiting higher susceptibility compared to the northwestern Everglades region. Overall, this study presents a robust urban flood susceptibility framework that supports improved flood risk assessment and decision-making in complex urban coastal environments.

The mapping and assessment of flood susceptibility is an integral component of flood mitigation and prevention programs, by determining the most vulnerable regions and the associated characteristics that influence the flood susceptibility. Hence, the aim of the present study is to identify flood-prone areas in the Fez-Meknes region (Morocco) for the first time using a multicriteria approach, in particular the Analytical Hierarchy Process (AHP) technique and Geographic Information Systems (GIS). A total of fifteen conditioning factors for flooding were selected: distance to rivers, river network density, precipitation, flow accumulation, elevation, slope, plane curvature, TWI, aspect, NDVI, LULC, TRI, geology, soil type, and SPI. All factors were defined as raster data sets with a resolution of 30 x 30 m. The results showed that, the efficiency tests of the flood susceptibility map show a good accuracy using an area under the curve (AUC) by remarkably good number (0.90).in addition, LULC was recognized as the most significant factor, which is followed by the stream power index that affect the flood map.

Flash floods are a significant risk in arid regions, especially in the Mekerra Basin of Northwestern Algeria, where rapid urbanization and climatic variability contribute to increasing flood vulnerability. This study integrates the Analytic Hierarchy Process (AHP) with Geographic Information Systems (GIS) to assess flood risk susceptibility in the basin. Eight key factors including, elevation, slope, drainage density, distance from rivers, flow accumulation, land use, topographic wetness index (TWI), and maximum daily rainfall were evaluated to calculate a Flood Susceptibility Index (FSI), categorizing the basin into flood risk levels. The findings show that 52.30 % of the basin is at moderate risk, while 42.37 % is at low risk. High-risk zones, representing 4.72 % of the area, are mainly located near the main wadi, where low-lying terrain, dense drainage networks, and urbanization contribute to increased flood vulnerability. The model achieved an 81 % accuracy when validated against historical flood data from 1986 to 2007, using a comparison of predicted flood-prone areas with observed flood occurrences. High-risk zones near the main wadi require immediate mitigation, such as reinforced infrastructure, while moderate-risk areas need targeted flood measures like improved drainage systems. Low-risk areas should focus on long-term strategies, including land-use planning and maintaining green spaces. The results highlight the combined impact of human activities and natural factors on flood risk, underscoring the importance of improving infrastructure and land-use planning, particularly in urban areas like Sidi Bel Abbes a major city in the basin. These findings underscore the need for targeted flood mitigation in urban areas and offer guidance for policymakers in improving infrastructure, land-use planning, and resilience against floods in the Mekerra Basin and similar regions. • Flood susceptibility in the Mekerra Basin in Algeria was evaluated, through the integration of the AHP and GIS techniques. • Eight flood-conditioning factors were examined to construct a Flood Susceptibility Index (FSI). • 52.30 % of the basin is classified as moderately susceptible to flooding, while 42.37 % falls within the low-risk category. • High-risk areas, comprising 4.72 % of the basin, are mainly located in low-lying urban area with dense drainage networks. • Model validation using historical flood data demonstrated an 81 % accuracy, confirming the reliability of the approach.

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas.

In Morocco, floods occur frequently, often causing significant damage to infrastructure and the environment due to a lack of adequate protective measures.The unpredictability of these events is attributable to climate change and the irregular nature of weather conditions.However, determining flood susceptibility can facilitate the mitigation and prevention of risk.This study aims at mapping flood-susceptible areas in the Bouarfa watershed using a multi-criteria decision analysis (MCDA) approach integrated within a Geographic Information System (GIS).Seven key conditioning parameters were considered: altitude, slope, geology, drainage density, flow accumulation, land use/land cover (LULC), and soil.The Analytical Hierarchy Process (AHP) was used to assign weights to these factors.The results obtained demonstrate that 39.53% (546.24km 2 ) of the territory is exposed to a very low to moderate flood risk and 60.47% (835.59km 2 ) to a high to very high flood risk.The model's accuracy was validated using historical flood locations and the Area Under the Curve (AUC) method, which yielded a value of 84.5%, indicating very good performance.This map serves as a critical tool for decision-makers for risk mitigation and land-use planning in this arid region.

Abstract. Floods have caused significant socio-economic damage and are extremely dangerous for human lives as well as infrastructures. The aim of this study is to use machine learning models including regularized random forest (RRF) and Naïve Bayes (NB) algorithms to predict flood susceptibility areas using 410 sample points (205 flood points and 205 non-flood points). Ten flood influencing factors including elevation, topographic wetness index, rainfall, normalized difference vegetation index, curvature, land use, distance to river, slope, lithology, and aspect have been used in the modelling process. For this purpose, 70% of the data was used for training and the rest employed for testing the models. Accuracy (ACC), sensitivity, specificity, negative predictive value (NPV), and the area under the curve (AUC) of the receiver operating characteristic (ROC) were used to validate and compare the performance of the models. The results showed that the RRF model on the testing dataset had the highest performance (AUC = 0.94, ACC = 90%, Sensitivity = 0.89, Specificity = 0.92, NPV = 0.89) compared to that of the NB model (AUC = 0.93, ACC = 89%, Sensitivity = 0.84, Specificity = 0.96, NPV = 0.81). The employed models can be used as an efficient tool for flood susceptibility mapping with the purpose of planning to reduce the damages.

This study aims to develop a comprehensive approach including an analysis of the relationships between flood susceptibility and land-use change, based on the Relevance Vector Machine (RVM) and Coyote Optimization Algorithm (COA) models, applied to Gianh river watershed, Quang Binh province, Central Vietnam. Standard statistical indices, i.e. area under the curve (AUC), were used to assess the model performance. Comparative analyses emphasize that the COA successfully improves the performance of RVM model (AUC=0.99) and is also better than the reference models such as Support Vector Machine (AUC=0.98), Gradient Boosting Machine (AUC=0.97), Random Forest (AUC=0.99), Extra Trees Regressor (AUC=0.98), and Adaboost (AUC=0.96). The improved model, when used in conjunction with land use maps, is able to show that urbanization has increased in flood susceptible areas. The results highlighted that, urbanization has increased in the low and very low flood susceptibility area by 110% between 2005 and 2020, while in the high and very high area by 30 to 40%, despite urban and demographic growth.

Flooding is considered as the most dangerous natural catastrophe in the world. This paper develops a spatial multi-criteria decision production prototype for the evaluation of flooding susceptibility for the city of Tunis. Eight criterions were considered in order to identify and assess the spatial distribution of hazardous zones and were integrated in the Geographical Information System (ArcGIS). The analytical hierarchy process (AHP) is a mathematical technique for flood hazard modeling and decision support based on the weight and rank of each flood factor in order to calculate Flood Hazard Index (FHI) to generate a flood susceptibility map. This was classified from very low to very high classes flood potential. Five classes are observed: 2.85% (very low), 7.5% (low), 17.95% (moderate), 20.63% (high), and 51.06% (very high). FHI was coupled with a sensitivity analysis to derive the Flood Hazard Index with sensitivity (FHIS). This index evaluates the effect of each criteria which allows for better assessment of the role of each parameter in flood susceptibility. The results of this study provide a basis to limit the impacts of flood and protect urban zone against inundation.

Globally, floods as dynamic hydraulic hazard have caused widespread damages to both socioeconomic conditions and environment at various scales. Managing flood and management of water resource is a global challenge under the changing climatic condition. This study assessed flood susceptibility in the Bhagirathi sub-basin, India using entropy information theory and geospatial technology. Twelve flood susceptibility parameters such as land use/land cover, normalized difference vegetation index (NDVI), slope, elevation, geology, geomorphology, normalized difference water index (NDWI), soil, drainage density, average rainfall, maximum temperature, and humidity during monsoon season were utilized to examine flood susceptibility. Receiver operating characteristics (ROC) curve and Leave-One-Out Cross-Validation (LOOCV) techniques were carried out to validate flood susceptibility map. Kappa statistics was also used to check the reliability of the flood susceptibility model. Findings of the study revealed that nearly 45% area of the sub-basin was highly susceptible to flood followed by moderate (29.3%), very high (19%), low (6.9%), and very low (0.2%). These findings also revealed that nearly 92% area in the eastern, north-eastern, and deltaic sub-basin was susceptible to floods. ROC analysis indicated high success (0.932) and prediction (0.903) rates for the susceptibility map while LOOCV (R2 being 0.97) and Kappa (k=0.934) have shown substantial prediction of the model. Hence, the susceptibility maps are useful for the local planners and government organization in designing the early flood warning system, and reducing the human and economic losses. The methodology used in this study is applicable for analyzing flood susceptibility at spatial scales in similar systems.

Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.

Floods are one of the most widely distributed hazards globally, and their management is an essential issue of concern among all stakeholders, especially in developing countries. Flooding is one of the most significant natural hazards affecting the Tsholotsho District, causing widespread damage to infrastructure and the natural environment and endangering people's lives. This study assessed flood hazards and risk areas in the District using remote sensing (RS), Geographic Information Systems (GIS), and analytical hierarchy process (AHP). Six main factors were considered to identify flooding hazard wards in the District: drainage density (DD), flow length, elevation, Normalized difference, vegetation index (NDVI), slope, and distance from the river. The study also identified five flood risk zones: high, moderate, low, and very low. The result of the flood risk map revealed that the low-risk area was 433699.57 hectares (56%), the moderate-risk area was 278328.41 hectares (36%), and the very high risk was 41060.25 hectares (5%) of the District. The results indicate that low-lying areas, mainly adjacent to major rivers such as the Gwayi and Manzamnyama Rivers, are highly susceptible to flooding, exacerbated by land degradation and deforestation. The study highlights the critical role of integrating GIS and remote sensing in flood risk assessment, providing decision-makers with valuable insights for developing early warning systems, flood hazard zoning, and sustainable land management practices. Recommendations for improving flood resilience in Tsholotsho District include community-based disaster risk reduction programs and the implementation of adaptive infrastructure.

This article presents a geographic information system (GIS)-based artificial neural network (GANN) model for flood susceptibility assessment of Keelung City, Taiwan. Various factors, including elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index, were generated using a digital elevation model and LANDSAT 8 imagery. Historical flood data from 2015 to 2019, including 307 flood events, were adopted for a comparison of flood susceptibility. Using these factors, the GANN model, based on the back-propagation neural network (BPNN), was employed to provide flood susceptibility. The validation results indicate that a satisfactory result, with a correlation coefficient of 0.814, was obtained. A comparison of the GANN model with those from the SOBEK model was conducted. The comparative results demonstrated that the proposed method can provide good accuracy in predicting flood susceptibility. The results of flood susceptibility are categorized into five classes: Very low, low, moderate, high, and very high, with coverage areas of 60.5%, 27.4%, 8.6%, 2.5%, and 1%, respectively. The results demonstrate that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area including the financial center of the city, can be categorized as high to very high flood susceptibility zones.

Riverbank erosion, soil loss, and flooding are interrelated processes that critically influence river morphology, sediment dynamics, and floodplain stability. Understanding their spatial and temporal interactions is essential for effective watershed management. This study evaluates the spatio-temporal patterns of river bank change along the Upper Ghaghara River basin and investigates their relationship with regional soil erosion risk and flood susceptibility. River bank dynamics were quantified using the Digital Shoreline Analysis System (DSAS v. 6.0) applied to multi-decadal satellite imagery from 1991 to 2024, calculating key metrics including End Point Rate (EPR), Net Shoreline Movement (NSM), and Shoreline Change Envelope (SCE). Soil erosion risk was modeled using the Revised Universal Soil Loss Equation (RUSLE), incorporating rainfall erosivity, soil erodibility, slope, vegetation cover, and conservation practices, while flood susceptibility was assessed by integrating multiple hydro-geomorphic factors using the Analytic Hierarchy Process (AHP). Results indicate pronounced erosion along the right bank with an average EPR of − 20.76 m/yr and an average NSM retreat of − 687.4 m, compared to − 5.53 m/yr and − 176.7 m, respectively, on the left bank. Approximately 62.7% of right bank transects and 57.5% of left bank transects exhibit significant erosion. Soil erosion rates exceed 40 t/ha/yr in high-risk zones of the basin spatially coincident with active bank migration areas. Flood susceptibility mapping identifies 30% of the basin under high to very high flood risk, corroborated by 2022 pre- and post-flood inundation analyses showing extensive floodplain coverage. These findings highlight the synergistic impact of bank erosion, upland sediment supply, and flooding on river corridor dynamics. The integrated assessment underscores the necessity for multi-hazard management strategies to mitigate geomorphic hazards, sustain riverine ecosystems, and enhance socio-economic resilience in this vulnerable basin.

Due to the physical processes of floods, the use of data-driven machine learning (ML) models is a cost-efficient approach to flood modeling. The innovation of the current study revolves around the development of tree-based ML models, including Rotation Forest (ROF), Alternating Decision Tree (ADTree), and Random Forest (RF) via binary particle swarm optimization (BPSO), to estimate flood susceptibility in the Maneh and Samalqan watershed, Iran. Therefore, to implement the models, 370 flood-prone locations in the case study were identified (2016–2019). In addition, 20 hydrogeological, topographical, geological, and environmental criteria affecting flood occurrence in the study area were extracted to predict flood susceptibility. The area under the curve (AUC) and a variety of other statistical indicators were used to evaluate the performances of the models. The results showed that the RF-BPSO (AUC=0.935) has the highest accuracy compared to ROF-BPSO (AUC=0.904), and ADTree-BPSO (AUC=0.923). In addition, the findings illustrated that the chance of flooding in the center of the area in question is greater than in other points due to lower elevation, lower slope, and proximity to rivers. Therefore, the ensemble framework proposed here can also be used to predict flood susceptibility maps in other regions with similar geo-environmental characteristics for flood management and prevention.