Enhancing precision flood mapping: Pahang’s vulnerability unveiled

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.

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

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.

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.

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.

A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems

Floods are among the most frequently occurring natural disasters and the costliest in terms of human life and ecosystem disturbance. Identifying areas susceptible to flooding is important for developing appropriate watershed management policies. As such, the goal of the present study was to develop an integrated framework for flood susceptibility assessment in data-scarce regions, using data from the Haraz watershed in Iran. Flood-influencing indices best suited to the identification of areas particularly prone to flooding were selected. The decision-making trial and evaluation laboratory (DEMATEL) approach was used to investigate the interdependence among criteria and to develop a network structure representative of the problem. The relative importance of different flood-influencing factors was determined using the analytical network process (ANP). A flood susceptibility map was produced using weights obtained through the ANP and fuzzy-value function (FVF) and validated using 72 available flood locations where flooding occurred between 2006 and 2018. After validating the results, fuzzy theory served to better delineate the flood susceptibility scores among the region’s sub-watersheds. Incorporating the DEMATEL-ANP approach with FVF yielded an accuracy of 89.1%, as was assessed through the area under the curve (AUC) of a receiver operating characteristics (ROC) curve. The results indicated that the strongest flood-influencing (occurrence/nonoccurrence) factors were elevation, land use, soil texture, and frequency of heavy rainstorms. The fuzzy theory showed sub-watershed C1 to be highly susceptible to flooding, and thus, most in need of flood management.

Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment

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.

ABSTRACTSince flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871).

Climate extreme events such as floods and droughts in any area have a significant impact on human life, infrastructure, agriculture, and the economy. In the last two years, flash floods caused by heavy rainstorms have become frequent and destructive in many catchments in Northern Iraq. The present study aims to examine flash floods in the Erbil region, Northern Iraq using Remote sensing (RS), Geographic Information System (GIS), and Principal Component Analysis (PCA) for geomorphic data. PCA results revealed that 12 geomorphic parameters exhibited a significant correlation with two different statistical components. To facilitate practical application, ranks are assigned based on the calculated parameters for flood susceptibility mapping. Out of the 24 basins in the current study, three basins (16, 3, and 14) have the highest geomorphometric values (36–39), indicating the zone most susceptible to flash floods and making up a maximum area of 38.58% of the studied region. Six basins (4, 8, 9, 10, 12, and 15), which have geomorphometric values between 30 and 35 and cover a land area of 27.86%, are the most moderately vulnerable to floods. The remaining basins, which make up 33.47% of the research, are occasionally subject to floods and have geomorphometric scores below 30. The precision of the flood susceptibility mapping was validated using the bifurcation ratio and drainage density relationship as well as past flood damages, such as economic losses and human casualties. Most of the recorded injuries and fatalities took place in areas that were particularly prone to severe past flooding. Additionally, the investigation revealed that 44.56% of all populated areas are located in extremely vulnerable basins. The findings demonstrate a notable correlation between the identified flood-susceptible areas and the occurrence of past flood damage.

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.

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.

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.