Flood susceptibility prioritization in Dimoria block Assam using GIS and analytical hierarchy process

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.

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.

Flood can be assessed through flood vulnerability, risk, and susceptibility analysis using remote sensing, geographic information system, and hydrological modelling. In this chapter, different stages, complexities, and processes of flood vulnerability, risk, and susceptibility assessment were discussed. The study reveals that flood vulnerability should be assessed based on four aspects: physical, social, economic, and environmental. Flood risk should be assessed by three stages: risk analysis, disaster relief, and preparedness, whereas flood susceptibility assessment involves three processes. Overall, it was found that the responsible factors vary as per the local conditions, which need to be carefully analyzed and selected. Furthermore, the role of remote sensing and geographic information system in flood risk management were found important especially in flood risk mapping and in the selection of responsible flooding factors.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of ’very high’ and ’high’ class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Flood is the most frequent and destructive natural disaster, causing significant negative impacts on humans and built and natural ecosystems. While it is extremely challenging to prevent floods, their associated hazards can be mitigated through well-planned and appropriate measures. The present study combined the analytical hierarchy process (AHP) analysis and an ArcGIS-based multi-criteria decision-making (MCDM) approach to assess, categorize, quantify, and map the flood-prone areas in Khyber Pakhtunkhwa, Pakistan, a region particularly vulnerable to recurrent flooding. Eight key factors including precipitation, rivers/streams, slope, elevation, soil, normalized difference vegetation index, and land use were used for flood susceptibility modeling. The weighted sum overlay tool in global positioning system ArcGIS was utilized to give weightage to each raster layer, based on the AHP ranking to produce a flood susceptibility map for the study area. According to the AHP analysis, the most impactful factors defining the flood susceptibility in our study area were streams (0.29%), precipitation (0.23%), slope of the area (14%), and LST (10%). Our flood model achieved excellent accuracy, with Area Under the Curve (AUC) value of 0.911. The model predicted that 9% of the total area is classified as very high risk, while 14% is identified as high risk, covering approximately 923,257 hectares and 1,419,480 hectares, respectively. These high-risk zones are predominantly concentrated in the central and lower northern, densely populated districts of the province. Our flood susceptibility results would assist policymakers, concerned departments, and local communities in assessing flood risk in a timely manner and designing effective mitigation and response strategies.

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.

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.

The sustainable development of coastal peri-urban areas is associated with the awareness of the local vulnerability to natural disasters, such as flood hazards. In ungauged basins, statistical and modelling approaches combined with geographic information systems have been widely used for the evaluation of different scenarios. In the current study, a multicriteria decision analysis (MCDA) simulated the response of the floodplains to the variations of the most dominant criteria, which affected the flood susceptibility of the area. An index of flood susceptibility is proposed to compare the simulated outcomes of the different scenarios. We provide a thorough discussion about improving the accuracy of the MCDA method through calibration and validation procedures, in association with the HEC-RAS 2D model. Observed and simulated inundation depths were compared for the validation process. The derived maps effectively revealed the flood susceptibility of the area upon the criteria variation, particularly the elevation and the rainfall.

Quantitative assessment of the relative impacts of climate change and human activity on flood susceptibility based on a cloud model

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

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.

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.

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.

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).

Flash floods are among the most severe hazards which have disastrous environmental, human, and economic impacts. This study is interested in the characterization of flood hazard in Gabes Catchment (southeastern Tunisia), considered as an important step for flood management in the region. Analytical hierarchy process (AHP) and geographic information system are applied to delineate and characterize flood areas. A spatial database was developed based on geological map, digital elevation model, land use, and rainfall data in order to evaluate the different factors susceptible to affect flood analysis. However, the uncertainties that are associated with AHP techniques may significantly impact the results. Flood susceptibility is analyzed as a function of weights using Monte Carlo (MC) simulation and Global sensitivity analysis. AHP and MC–AHP models gave similar results. However, compared to AHP approach, MC–AHP confidence intervals (95%) of the overall scores had small overlaps. Results obtained were validated by remote sensing data for the zones that showed very high flood hazard during the extreme rainfall event of June 2014 that hit the study basin.