A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment

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.

Floods, which are common than other natural disasters like earthquakes, heavy precipitations, and droughts, are one the primary effects of global climate change and have major effects on human safety, sustainable development, and economic growth. As climate warming and intensifying hydrologic cycle worsen, global flooding risks may increase, potentially impacting Afghanistan as well. Severe flooding being caused by rising temperatures, erratic rainfall patterns, and extreme weather in Afghanistan, especially in the region of Kandahar. Despite the significance of identifying and mapping flood – prone areas, this province has not participated in any previous studies done on the topic at hand. Therefore, the aim of this research was to develop a flood susceptibility map for Kandahar province and identified flood – prone areas with high levels of occurrence by integrating Geographic Information System (GIS) and Multi – Criteria Decision – Making (MCDM) method, with Analytic Hierarchy Process (AHP). To achieve the study’s goal, 11 Flood Causative Factors (FCFs), such as runoff potential, slope, rainfall, flow accumulation, distance from rivers, topographic wetness index (TWI), drainage density, lithology, Digital elevation model (DEM), sediment transport index (STI), and curvature, were weighted and overlayed. The resulting map depicted five different levels of susceptibility to flooding: least, low, moderate, high, and very high. The model’s final map of flood susceptibility was found to be in line with past flood occurrences in the study area, demonstrating the successful outcome of the methodology utilized to locate and map flood – prone areas.

In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is fundamental for decision makers. To assess flood-prone locations in the Akre, Iraqi Kurdistan Region, fundamental for susceptibility mapping was undertaken using geographic information systems, remote sensing, and an analytic hierarchy process model. To assess flood susceptibility, the geographic information systems framework used 15 ideal causative factors for flooding: altitude, slope, distance to streams, flow accumulation, drainage density, rainfall, soil type, lithology, curvature, topographic wetness index (TWI), topographic roughness index stream power index, stream transport index, land use/land cover, and normalized difference vegetation index. The factors contributing to flooding were optimally weighted with respect to the proposed model. The final flood susceptibility map was reclassified into five different classes of susceptibility to flooding: very low (16.64% of the study area); low (19.53%); moderate (38.92%); high (17.83%); and very high (7.08%). The area under curve values for the predicted rate and success rate of the AHP model were 0.956 and 0.971, respectively. Therefore, the results were accurate and reliable. The AHP model is a powerful method for fundamental for susceptibility mapping to mitigate the serious impacts of flooding and assist scholars, local governments and policymakers in future master planning.

This study presented herein compares the effect of the sampling strategies by means of landslide inventory on the landslide susceptibility mapping. The conditional probability (CP) and artificial neural networks (ANN) models were applied in Sebinkarahisar (Giresun–Turkey). Digital elevation model was first constructed using a geographical information system software and parameter maps affecting the slope stability such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index and normalized difference vegetation index were considered. In the last stage of the analyses, landslide susceptibility maps were produced applying different sampling strategies such as; scarp, seed cell and point. The maps elaborated were then compared by means of their validations. Scarp sampling strategy gave the best results than the point, whereas the scarp and seed cell methods can be evaluated relatively similar. Comparison of the landslide susceptibility maps with known landslide locations indicated that the higher accuracy was obtained for ANN model using the scarp sampling strategy. The results obtained in this study also showed that the CP model can be used as a simple tool in assessment of the landslide susceptibility, because input process, calculations and output process are very simple and can be readily understood.

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.

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.

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures.


 
 
 Flood susceptibility analysis (FSA) is a prerequisite for adopting flood mitigation and adaptation techniques. A number of technologies and models have emerged through time, and this study focuses on the Binary Logistic Regression Model (BLRM) to investigate flood vulnerability of Elapatha Divisional secretariat (DS), Ratnapura District, Sri Lanka where has high impact from inundation. Rainfall, land use and land cover (LULC), elevation, slope, slope aspect, distance to the river, topographic wetness index (TWI), and stream power index (SPI) were the factors used in the model construction. These components were investigated in terms of their contribution to flood susceptibility using all location data and field plotting of responsible parameters in the study area using Geographic Information System (GIS) software, and all extracted data points were 96489, of which 50% were used for BLRM development using SPSS statistical software and remaining 50% for model validation. The coefficient of rainfall parameters, log value of elevation in meters, the tan value of slope in degrees, radiant value of aspect in degrees, (distance from the river)0.1, the ratio between SPI and TWI (SPI/TWI) and LULC band values of built-up area, water bodies and vegetations were 0.023, -2.254, -1.018, -0.005, -0.164, -0.003, 2.707, -.067 and -0.004 respectively. The accuracy was validated using Mean standard error (MSE) and area under curve (AUC) analysis, with values of 0.031 and 0.724 respectively. The Built-up area, elevation, and slope had the most impact on the inundation Elapatha DS division, and model performance represents 72.4% accuracy. Therefore, mitigation of inundation problems can be achieved through proper landscaping in the area.
 Keywords: Binary logistic Regression, Flood susceptibility Analysis, GIS software, Spatial analysis
 
 

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.

Qilian Mountains is an important water conservation area in Northwest China, which is the boundary between the first and second steps of China's topography and is sensitive to climate change. Based on the data of temperature, precipitation, normal difference vegetation index (NDVI), and digital elevation model (DEM) data, we analyzed NDVI change and its relationship with temperature and precipitation along the elevation, slope and slope aspect in the southern slope of Qilian Mountains using tendency analysis method, wavelet analysis and correlation analysis. The results showed that, from 1998 to 2017, NDVI value of the growing season presented increasing trend by a rate of 0.023·10 a-1. Changes of NDVI differed at different elevations, slopes and slope aspects. NDVI increased first and then decreased with elevation. The vegetation coverage at 2700-3700 m was good, and degraded in the area of >4700 m. NDVI reduced with the increases of slope, which showed little difference in different slope aspects but was better in sunny slope than in shade slope. NDVI of the growing season was closely related with temperature and precipitation. NDVI, temperature and precipitation in growing season all had a 14-year cycle. Vegetation at different elevations, slopes and slope aspects was differently affected by temperature and precipitation. Vegetation in areas with altitude <3700 m, >4700 m, slope <25° and each slope direction was more sensitive to precipitation.

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.

Increasing the occurrence of floods, especially in cities, and the risks to human, financial, and environmental risks due to its, make flood risk zoning of great importance. The purpose of this study is to estimate the flood risk of the Maneh and Samalghan based on determining effective criteria and spatial and non-spatial data-driven models. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present new combination approaches to determine the effective criteria in flood risk zoning (Maneh and Samalghan). In this regard, the geographically weighted regression (GWR) with exponential and bi-square kernels and artificial neural network (ANN) combined with a binary particle swarm optimization algorithm (BPSO). The best value of the fitness function (1-R2) for ANN, GWR with the exponential kernel, and GWR with bi-square kernel was obtained 0.1757, 0.0461, and 0.0097, respectively, Which indicates higher compatibility of the bi-square kernel than the other models. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.