Climate change and flood susceptibility in Bocas del Toro, Panama: A multi-criteria spatial analysis approach.

Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP)

Floods, as recurrent natural disasters, exert significant impacts on both the environment and human settlements. This study focuses on evaluating the flood susceptibility of the Bhadradri Kothagudem district in Telangana, situated within the Godavari River Basin. The research integrates Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) in a multi-criteria approach to analyze and map flood susceptibility. Landsat-8 data, digital elevation model data and rainfall serve as inputs for assessing the flood susceptibility. The study considers various topographical features such as elevation, slope, roughness, contours and aspect, along with factors like land use land cover, flow accumulation, stream direction, stream network, drainage density, flow length, distance from the river, soil, normalized difference vegetation index and topographic wetness index. These variables are rescaled on a scale of one to five and combined to generate a comprehensive flood susceptibility map of the Kothagudem district using GIS. The AHP is implemented through GIS, assigning weightages on a scale of one to five based on the priority of spatial classes within thematic maps. The flood susceptibility map is produced on a scale of five, designating scale class five as of very high susceptibility and scale class one as low. Scale classes two, three and four represent intermediate levels of susceptibility. The resulting flood susceptibility maps offer valuable insights for disaster preparedness, risk mitigation and land-use planning in the Kothagudem district. The integration of GIS and AHP provides a robust methodology for assessing and visualizing flood susceptibility, enabling informed decision-making for resilient and sustainable development in flood-prone regions.

This manuscript presents a comprehensive study on flood susceptibility mapping in the Greater Hyderabad Municipal Corporation (GHMC) region of Telangana State, India, utilizing Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) as a multi-criteria approach. The primary objective of this research is to develop a robust flood susceptibility mapping framework for GHMC, considering various thematic maps including distance to river, elevation, flow accumulation, flow direction, drainage density, contour, Landsat 8, normalized difference vegetation index (NDVI), land use and land cover (LULC), annual rainfall, roughness, slope, stream network, topographic wetness index (TWI) and flood susceptibility map. Through the integration of these thematic maps, we have successfully delineated areas prone to flooding within the GHMC region. The study highlights the importance of utilizing multiple criteria and GIS techniques for accurate flood susceptibility assessment. The results indicate that areas with high drainage density, low elevation and proximity to rivers are more susceptible to flooding. Moreover, factors such as land cover, rainfall intensity and terrain roughness significantly influence flood susceptibility. Conclusions drawn from this study emphasize the significance of incorporating spatial analysis and decision-making techniques in flood risk management and urban planning initiatives.

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge

Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India

ABSTRACTFlooding poses a persistent challenge in Bangladesh, where complete prevention remains difficult due to its geographical and climatic conditions. This study integrates the Analytical Hierarchy Process (AHP) with Geographic Information System (GIS) techniques to create a detailed flood susceptibility map for the Sylhet division in northern Bangladesh. The primary goal is to classify the region into distinct flood susceptibility zones, providing valuable insights for improving flood risk management, mitigation, and preparedness strategies. The study evaluates 12 critical flood‐influencing parameters, including elevation, slope, topographic wetness index (TWI), precipitation, drainage density, proximity to roads and rivers, vegetation, land use and land cover (LULC), and soil type. These factors were chosen based on their established relevance to flood dynamics, with data sourced from reliable spatial databases to ensure accuracy. Using AHP, weights were assigned to each parameter based on expert input, reflecting their relative importance in flood risk. These weighted factors were then integrated using GIS overlay analysis and weighted linear combination techniques to generate a flood susceptibility map. The results show that approximately 35.27% of the Sylhet division, particularly the northern regions and the low‐lying Haor basin, fall into the “high” flood susceptibility categories. These areas are highly vulnerable due to their flat topography, proximity to major rivers, and inadequate drainage systems. In contrast, the southern and southwestern areas, accounting for around 7.45% of the region, exhibit “low” flood susceptibility, benefiting from higher elevations and better natural drainage. This flood susceptibility map serves as an essential tool for identifying high‐risk areas, supporting targeted flood mitigation efforts, and enhancing disaster preparedness. By providing a scientific foundation for effective flood management, the study aids decision‐makers in reducing flood impacts and promoting the sustainable development of flood‐prone regions in northern Bangladesh.

Flood susceptibility mapping and assessment is an important element of flood prevention and mitigation strategies because it identifies the most vulnerable areas based on physical characteristics that determine the propensity for flooding. This study aims to define the flood susceptibility zones for the territory of Slovakia using a multi-criteria approach, particularly the analytical hierarchy process (AHP) technique, and geographic information systems (GIS). Seven flood conditioning factors were chosen: hydrography—distance from rivers, river network density; hydrology—flow accumulation; morphometry—elevation, slope; and permeability—curve numbers, lithology. All factors were defined as raster datasets with the resolution of 50 x 50 m. The AHP technique was used to calculate the factor weights. The relative importance of the selected factors prioritized slope degree as the most important factor followed by river network density, distance from rivers, flow accumulation, elevation, curve number, and lithology. It was found that 33.1% of the territory of Slovakia is characterized by very high to high flood susceptibility. The flood susceptibility map was validated against 1513 flood historical points showing very good agreement between the computed susceptibility zones and historical flood events of which 70.9% were coincident with high and very high susceptibility levels, thus confirming the effectiveness of the methodology adopted.

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds.

Flooding has become more prevalent in many regions of Southeast Asian countries in recent decades. Intense precipitation, settlement in low-lying areas, population growth, and rapid urbanization can enhance vulnerability to floods and lead to serious hazards. This study developed flood susceptibility mapping for the Chai Nat province of Thailand using flood-conditioning factors and the frequency ratio (FR) method. The flood inventory (2005–2017) was randomly separated into a training dataset for FR analysis and a testing dataset for model validation. Eleven flood-conditioning parameters, i.e., altitude, slope, curvature, the topographic wetness index, rainfall, distance to drainage, drainage density, soil drainage, land use, the normalized difference vegetation index, and road density, were considered for this study. While constructing the flood susceptibility index, the relative frequency and predictor rate were used to create the flooding probability for each factor class and the weight of each factor in the model. The values for the flood susceptibility index were classified into five categories and used to make a flood susceptibility map. The area under the curve (AUC) was used to validate the model prediction. The results indicate that the AUC values for the success and prediction rates are 74.2% and 75.1%, respectively.KeywordsFlood susceptibilityFrequency ratioPredictor rateGISThailand

Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East

Flood susceptible prediction through the use of geospatial variables and machine learning methods

This study aims to assess the flood susceptibility analysis using a Geographical Information System (GIS) based-heuristic analysis, namely the Analytical Hierarchy Process (AHP) model. Eight relevant physical parameters have been selected, namely, drainage density, drainage proximity, elevation, slope angle, slope curvature, land use, soil type, and topography wetness index. The relative importance of these factors has been compared in the pairwise matrix to gain weight values during the process of the Analytical Hierarchy Process (AHP). The flood susceptibility zones have been mapped according to their weightage value. Finally, the flood susceptibility map was prepared and classified into six classes as very low, low, moderate, high, and very high susceptibility using the natural break classification method. The accuracy of the flood susceptibility model was validated using the Area Under the Curve (AUC) analysis. The AUC for success rate was estimated at 82.13%.7.

Among all the natural disasters, floods are the most common phenomena that cause huge obliteration to the human lives and socio-economic and cultural infrastructures. Silabati, a monsoon influenced river of West Bengal is well known for frequent flooding events in its lower basin areas. In the present study, an attempt has been made to delineate flood susceptible areas of Silabati river basin using AHP (Analytical Hierarchy Process) technique and geospatial technology. A total number of 11 physiographic, climatic, and anthropogenic factors (elevation, slope, flow accumulation, distance from river, drainage density, geomorphology, lithology, surface runoff, topographic wetness index, land use land cover, and curvature) are taken into consideration to prepare the flood susceptibility map of the study area. The map is categorized into five distinct flood susceptible zones, such as very high, high, moderate, low, and very low susceptible zones, and these zones cover 14.04%, 20.67%, 21.76%, 20.69%, and 22.84% of the total basin area, respectively. Keshpur, Ghatal, Chandrakona-I, Chandrakona-II, and Daspur-I Community Development (C.D.) blocks of West Medinipur district located in lower Silabati basin fall under very high and high flood susceptibility zones. The performance and efficiency of AHP are validated using Area Under Curve (AUC) method, which ensured significant accuracy (76.41%) of the study. A large number of people residing on lower Silabati basin along with several socio-economic and cultural structures get severely affected many times during floods. Therefore, this study may facilitate the formulation and implementation of flood management strategies in the vulnerable areas of Silabati river basin.KeywordsGeospatial technologyAHPCausative factorsFlood susceptibilitySilabati river