Flood and Water Logging Risk Assessment of Jashore District Leveraging the Analytical Hierarchy Process (AHP) Through a Spatial Analysis Approach

Flood and Water Logging Risk Assessment of Jashore District Leveraging the Analytical Hierarchy Process (AHP) Through a Spatial Analysis Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Flood and Water Logging Risk Assessment of Jashore District Leveraging the Analytical Hierarchy Process (AHP) Through a Spatial Analysis Approach Md Refath Hossan, Mohammad Ismail Hossain, Zahid Hassan Shaon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7106425/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Jashore district in Bangladesh is facing persistent problems of flooding and landslides due to heavy rainfall, inadequate drainage systems and poor management of its water table. These problems are undermining livelihoods, undermining infrastructure and threatening the sustainability of the region's economy. The study introduces an innovative methodology for assessing the risks of deforestation. A comprehensive analysis of ten key factors - topographic wetness index (13.8), elevation (12.1), slope (9.9), land use and area (6.6), normalized difference vegetation index (5.3) - has helped to create a detailed spatial risk map. The findings classify the district as follows: very low (364.20 km), low (1397.63 km 2), medium (273.02 km 2 ) and high (83.68 km). The high and very high risk areas, including the Kapalia area between Manirampur and Avoynagar, and Keshabpur Upazila, are characterized by low altitude, heavy rainfall, proximity to rivers and poor drainage. Conversely, low-risk areas such as the Chowgacha and Jhikargacha uplands have higher altitudes and more pronounced slopes. The analysis highlights the critical role of the topographical moisture index (TWI), with higher TWIs associated with less water scarcity, whereas elevation is the most important factor for water scarcity. The main cause of the flooding is the intensity of rainfall, compounded by poor drainage and poor management of waterways. Integrating AHP and GIS not only improves analytical accuracy, but also provides a visually intuitive and actionable tool for decision-makers. The study provides a visionary framework for tackling the root causes of flooding, promoting sustainable water management and driving forward climate change. The findings are a critical source for developing strategic interventions to mitigate the risks of flashpoint. Water Logging Risk Flood Prediction Model Analytical Hierarchy Process (AHP) Geographic Information System (GIS) Multi-criteria Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Floods are one of the most devastating natural disasters, causing widespread damage to communications networks, infrastructure and property. They seriously affect agricultural output, land, and other vital resources, and they result in large losses of human and animal life. [ 1 ]. The primary cause of these occurrences is abrupt, extensive, and recurring precipitation, particularly in tropical and subtropical regions where the monsoon is present [ 2 ]. Floods are common in Bangladesh, where June through September receives more than 80% of the country's yearly rainfall (Uddin & Matin, 2021). Flooding is made worse by this intense monsoon rains, tidal surges, cyclonic occurrences, upstream runoff, and poor drainage systems [ 3 ]Over the years, these occurrences have continuously caused significant harm to flood plains, deltas, and coastal regions around the nation. [ 4 ]. Bangladesh is especially susceptible to frequent flood disasters because of its primarily low-lying terrain [ 5 ]. Bangladesh is still especially susceptible to frequent flood disasters because of its primarily low-lying terrain [ 6 ]. This is also true in Bangladesh's southwest, particularly the Jashore District. It is especially susceptible to flooding and chronic water scarcity due to its geographic location, shifting climatic patterns, and human activities. According to historical records, there were large flooding incidents in 1988, 1998, 2004, 2007, and 2014 all of which resulted in severe infrastructure damage and deaths [ 7 ]. The Jashore region's floods are largely driven by runoff build-up in upstream areas quickly and the downstream excess water releasing slowly [ 8 ]. Periods of heavy and protracted precipitation often exacerbate these conditions, resulting in a complicated web of hydrological elements that raises the danger of flooding. When these mechanisms function in tandem, water levels frequently rise sharply before falling quickly [ 9 ]. Floods are made more severe by the inadequacy of the drainage network and the major rivers' inability to handle excessive water flows [ 10 ]. Adopting comprehensive flood management strategies and mitigation techniques is necessary to address these issues and reduce the negative effects on ecosystems, agriculture, and infrastructure [ 11 ]. Additionally, identifying areas prone to flooding is essential for creating focused treatments and efficient early warning systems. In order to strengthen resilience and shield vulnerable areas from the growing risks of floods, several proactive steps are required [ 12 ]. Floods are the result of multifactorial and dynamic processes, which make their prevention almost impossible [ 13 ]. As a result, minimizing the overwhelming effects of floods has become one of the most urgent global challenges [ 14 ]. Effective flood risk mitigation depends on rapid and precise risk evaluations, which play a key role in developing robust and actionable risk management strategies [ 15 ]. Implementation of advanced geospatial observation and GIS technology has transformed flood risk assessment, giving academics and practitioners significant tools for more accurate and efficient risk assessment [ 16 ]. Flood risks are evaluated by analyzing spatially susceptible regions that are particularly vulnerable to floods and where the potential for devastating consequences is greatly raised [ 15 ]. A comprehensive flood risk assessment is necessary for an efficient flood risk management system since it involves precise estimation of flood hazards, exposure, and susceptibility [ 17 ]. Flood risk assessment now includes geospatial technology like Remote Sensing (RS) and Geographic Information Systems (GIS). They are indispensable for enhancing the accuracy of flood vulnerability assessments and management because of their exceptional precision and accessibility due to their sophisticated skills in data integration, geographical analysis, and visualization (Luu et al., 2019;Tehrany et al., 2015). The analytical hierarchy process (AHP), a popular multi-criteria decision-making technique (MCDM), serves a fundamental function in recognizing and assessing the primary contributors to flood risk. The AHP simplifies the calculation of a comprehensive flood risk index by assigning weights and categories to many elements [ 19 ]. AHP's ability to tackle complicated decision-making problems, even with insufficient data, makes it a useful tool for mapping flood vulnerability (Fernández & Lutz, 2010; [ 18 ] In order to overcome these drawbacks, the paper presents a novel method for mapping flood susceptibility in Bangladesh's southeast and Jashore coastal regions by combining AHP with GIS. The flood risk index, which considers a complicated collection of factors such elevation, topographic wetness index, slope, drainage density, rainfall, distance from the road, closeness to the river, and soil properties, is calculated using a pairwise comparison approach. This advancement contributes meaningfully to the enhancement of environmental management, and socioeconomic resilience while offering a strong framework for mapping flood susceptibility. The findings of this study will be of great use to researchers, policymakers, and flood-management specialists, opening the door to more effective and comprehensive and durable flood resilience planning in Bangladesh's southern and Jashore coastal areas. 2. Materials and Methods 2.1 Study area The survey was conducted in the Jashpur area of southwestern Bangladesh. The district is bordered to the north by the Jhenaidah River and to the west by the West Bengal State. Jashore, which occupies 2,606.94 km2, is home to an estimated 2,764,547 people. It is administratively divided into 8 upazilas (subdivisions) and 92 union districts, located at 22.47 to 23.47 degrees north latitude and 88.40 to 89.50 degrees east longitude [ 21 ]. The Jashore is an integral part of the vast Ganges delta, which is known for its fertile alluvial deposits, which are the result of the downstream processes of sedimentation in the Ganges-Brahmaputra-Meghna basin. The district is crossed by important rivers such as the Bhairava, the Kapotaksha and the Ichamati, which are all siltated and combined with poor drainage infrastructure have exacerbated water scarcity. These factors, combined with heavy rainfall from the monsoon, contribute significantly to recurrent flooding and long-term water scarcity, which affects livelihoods and agricultural activity. The area is characterised by a tropical monsoon climate, with most precipitation occurring between June and September. Average annual rainfall is approximately 1,800 mm. Temperatures range from a balmy 43 degrees Celsius in April, the hottest month, to a chilly 7 degrees Celsius in January, the coldest month (Khan, 2012). The predominant land use in Jashore is agriculture, with almost 62.5 percent of the population engaged in agricultural activity. Rice, cotton and vegetables are the backbone of the local economy, but they are very vulnerable to the negative effects of flooding and droughts. In addition to crop production, fish farming is an increasingly important source of livelihood in the region. The extensive river systems and ponds of the Jashore area provide favorable conditions for aquaculture and offer an alternative source of income for the local population. However, the same problems of waterlogging and flooding also affect fish farming activities, leading to fish losses and affecting the economic stability of aquaculture users. The Jashore area's topographical and hydrological characteristics make it especially prone to floods due to a mix of causes such as poor drainage systems, river slippage, and overflow from upstream sources during the monsoon season. Recurrent waterlogging not only affects agricultural land, but it also causes significant socioeconomic problems in the sedimentary basin. Major floods and landslides in recent years have resulted in considerable crop and fish farm losses, highlighting the region's vulnerability and the urgent need for long-term flood mitigation solutions. These criteria support the Jashore area's selection as a study site for this research, which intends to provide information on waterlogging reduction, regional resilience development, and agricultural and aquaculture productivity. 2.2 Data Collection and Acquisition The collection and acquisition of data is critical in the early stages of a research approach. Comprehensive data sets will be created, including elevation, land use and area, slope, average precipitation, drainage density, distance from roads, proximity to rivers, topographic vegetation index (TOPI), normalized difference vegetation index (NDVI), and soil structure. These data sets will form a multidimensional foundation for conducting detailed geographical and environmental investigations. By combining these various factors, this phase provides a firm foundation for a scientifically sound investigation, ensuring methodological accuracy and increasing the overall legitimacy and usefulness of the research findings. This phase covers the collection of high-resolution satellite photos of the Jashore area to serve as the foundation for geospatial analysis. These images serve as the foundation for precise mapping and study of the battlefield's features. In addition, it is essential to obtain all necessary licenses and authorizations for the use of proprietary geospatial data. Compliance with these protocols not only upholds ethical standards of research, but also ensures compliance with intellectual property laws, thereby enhancing the integrity and credibility of the study. Table 1 Geospatial and Environmental data sources for flood risk assessment Sl.no Factors Data Type Data Sources 1 DEM 30×30, 1 Arc 30m US Geological Survey (USGS) Shuttle Radar Topography Mission (SRTM) data 2 Elevation, slope, TWI, drainage density 30×30 Generated from the DEM 3 LULC 10×10, Raster Image Environmental Systems Research Institute (ESRI) Sentinel-2data 4 Soil texture Shape File Bangladesh Agricultural Research Council (BARC) 5 Rainfall 20 Years average Rainfall (Shape File) CHRS data portal 6 Distance to rivers, distance to roads Shape File BBBike extracts OpenStreetMap 2.3 Data Preprocessing Following the acquisition of the data, the next step is data preparation, to prepare the data for analysis. It is important to eliminate any errors and inconsistencies in the datasets obtained in order to ensure the quality and accuracy of the data. Subsequently, different data sets will be integrated, including elevation, TWI, slope, LULC, NDVI, rainfall, drainage density, distance from roads, distance from rivers and soil structure. All data sets will be continuously projected and geo-referenced to the common spatial analysis coordinate system 2.4 Application of Analytical Hierarchy Process (AHP) One of the unique aspects of the science of flood and water management risk assessment is the creation of a number of conceptual frameworks which define different indicators and thus represent a discrete process of assessing vulnerability [ 22 ]. The present investigation is based on a methodology combining the AHP and the GIS. The RS data are collected and processed by the ArcGIS platform for the purpose of mapping flood vulnerability. Figure 2 shows the methodological steps, including data collection, data processing and profiling. The compilation of maps for several flood-related factors is the first step in the process of flood susceptibility mapping. The study used ten factors to influence flooding: dem, slope, drainage density, soil type, rainfall, topographic moisture index (TMI), land use and area (LULC), river distance and road distance. Table 1 shows the source of the collected data. After obtaining information from several sources, the GIS environment is used to process the collected maps and to create a topic map for each factor. ArcGIS software 10.8 is used to process data, analyses them and develop thematic layers in relation to the above-mentioned impact factors. A thematic map of different features is used to define sub-classes according to the impact of these aspects on flood vulnerability. Table 1 is an example of a table and Table 1 is an example of a table legend. The Analytical Hierarchical Process (AHP) is one of the most popular and successful multi-criteria decision analysis techniques (MCDA). Following the creation of thematic maps for the influencing factors, the AHP is used to weight the sub-categories of the factors. AHP helps decision makers to break complex issues down into their hierarchical structure by methodically identifying the essentials, such as goals, standards, and options. This method provides a robust framework for informed and logical decision making in research and analysis by facilitating prioritisation of components and their relationship[ 23 ]. Figure 3 displays the weights that were obtained from the primary eigenvector of the pairwise correlation matrix. A systematic method for quantitatively comparing decision criteria is a pairwise comparison matrix (Table 2 ). Table 2 displays the normalized pairwise comparison matrix and associated weights that were determined by the approximation method. With a weight of 0.14, distance from river was the most important factor; distance from river was the least important at 0.05. Table 2 gives details on the other weights for slope, TWI, elevation, precipitation, LULC, NDVI, drainage density and soil structure, while the plot below illustrates the latter. deductive remittance. The weighted-average maps were made of the elevation, slope, TWI, LULC, NDVI, drainage density, rainfall, distance from the river, distance from the roads, and soil structure. These maps were used as a starting point for further research. The AHP approach was used to make pairwise comparisons to determine the relative importance of the different components. Table 2 Scale for Degree of Significance in Pairwise Comparison (AHP) [ 24 ] Degree of Significance Definition (Preferences on a Scale) Description (Explanation) 1 Equally significant The two criteria have the same importance. 3 Moderate significance of one over another One criterion is moderately superior to another based on experience and judgment. 5 Critical or strong significance One criterion is essentially or strongly superior to another based on experience and judgment. 7 Very strong significance One criterion is very strongly superior to another based on experience and judgment. And its power is reflected in practice. 9 Extremely significant One criterion is extremely superior to another based on experience and judgment. And its power is reflected in practice. The evidence is of the greatest confirmation. 2, 4, 6, 8 Intermediate significance between two adjacent preferences Used to indicate the compromises between the above judgments (i.e., 1, 3, 5, 7, and 9). Numerous studies around the world use this ranking approach to rank elements according to their importance [ 25 ], [ 26 ], [ 27 ]. The parallel comparison matrix determines the weight or priority of the components in the AHP descriptor. According to Saaty, a linear scale of 1 to 9 is used to assign a score to create a randomized comparative matrix. According to the Saaty linear scale, 1 indicates equal importance, 9 indicates significantly more importance and 1 or 9 indicates extremely less importance. Protestant martyrdom. 4 lists the importance of each score. Figure 3 provides a pairwise matrix of several criteria. The priority of each factor is determined on the basis of the importance found in the pairwise comparison matrix (i.e. the AHP analysis). According to the Saaty approach, the optimal pairwise comparison matrix should be less than 10percent. The consistency index (CI) has been divided by the random index (RI) in Eq. (1) to determine the value of CR. $$\:\begin{array}{c}CR=\frac{CI}{RI}\#\left(1\right)\end{array}$$ The consistency ratio is represented by CR, the consistency index by CI, and the random index by RI, the value of which depends on how many parameters have been compared. The Eq. (2) was used to get the consistency index (CI) $$\:\begin{array}{c}CI=\frac{{\lambda\:}_{max}-n}{n-1}\#\left(2\right)\end{array}$$ The consistency index (CI) is calculated from the average eigenvalue (k) of the consistency vector, with n being the total number of thematic layers included in the analysis. According to the analytical hierarchy principle, the coefficient of variation (CR) should not exceed 10 percent to ensure the reliability of the weights derived (Saaty, 1990). The calculated CR in this study is 0.37 percent, which indicates a high level of consistency and reliability in the rating process. For the compilation of the composite flood vulnerability map, each of the class factor maps was multiplied by its respective weight using the weighted sum method. The weighted maps were then added together as shown in Eq. 3 to form the final composite map. $$\:\begin{array}{c}FS=\sum\:Wi\times\:Xi\#\left(3\right)\end{array}$$ Where FS stands for flood susceptibility, Wi for factor i’s weight, and Xi for each factor i’s classes of susceptibility. Next, weight is allocated to each factor’s subcategories according to its ranking priority, as shown in Table 4 . In order to create the flood susceptibility map, the weighted overlay analysis of the components is then carried out on the ArcGIS platform using the provided weighting. 2.5 Geospatial Analysis The vulnerability to floods in the study area will be systematically assessed through a framework of multi-criteria geospatial analysis, integrating spatial data sets with weighted factors derived by the analysis hierarchy process. This methodological approach allows quantitative and reproducible flood risk assessments by synthesizing a variety of environmental and anthropogenic variables. The basic analytical technique used in this process will be overlay analysis, which makes it possible to superimpose several thematic layers to determine spatial patterns of flood vulnerability. The geospatial data sets to be integrated include Sentinel-2A high resolution images, digital elevation models (DEM), land use and land cover (LULC), slope gradients, rainfall intensity, drainage densities and proximity indices such as road and river distances. These data sets will be standardized and integrated using spatial modeling techniques based on the doctrine of sovereignty. The resulting comprehensive flood risk maps will reveal spatial diversity and complex interactions between contributing factors, and provide a detailed representation of flood risk areas. This integrated approach, combining empirical data, expert weighting of AHP and advanced spatial analysis, will culminate in the development of flood risk maps for the Jordon area. Each defined area will reflect a specific level of vulnerability and will support evidence-based planning and targeted disaster risk reduction strategies. 2.6 Slope Slope is an important topographic feature that influences flood behavior since it directly impacts surface runoff velocity [ 28 ]. Steep slopes promote faster water circulation and limit infiltration, making such regions less prone to floods [ 29 ]. Flat or moderately sloping terrains, on the other hand, retain more runoff, increasing the likelihood of floods caused by water accumulation [ 30 ]. The slope layer was produced from the Digital Elevation Model (DEM) using ArcGIS surface analysis tools. The slope values were then divided into five flood susceptibility classes using natural breaks: very high (0-1.13%), high (1.13–2.06%), moderate (2.06–3.20%), low (3.20–4.85%), and very low (5.85–26.35%) (Fig. 4 a). Each class was given a score ranging from 1 to 5 to describe its relative influence on flooding, with 5 (extremely low slope) indicating the most flood risk and 1 (very high slope) indicating the lowest. Table 3 Weights assigned to thematic layers for flood risk assessment. No Flood Causative Criterion Unit Class Susceptibility Class Ratings and Ranges Susceptibility Class Rating Weight % 1 Topographic Wetness Index (TWI) Level -7.07 -3.34 Very Low 1 13.8 -3.34 -0.96 Low 2 0.96–1.34 Moderate 3 1.34–4.10 High 4 4.10-11.93 Very High 5 2 Elevation m -23-3 Very High 5 12.1 3–6 High 4 6–9 Moderate 3 9–12 Low 2 12–34 Very Low 1 3 Slope % 0-1.13 Very High 5 9.9 1.13–2.06 High 4 2.06–3.20 Moderate 3 3.20–4.85 Low 2 4.85–26.35 Very Low 1 4 Precipitation mm/20 years Average 27123–27789 Very Low 1 13.5 27789–28173 Low 2 28173–28554 Moderate 3 28554–28980 High 4 28980–29462 Very High 5 5 LULC Level Water Body Very High 5 6.6 Wetland High 4 Agricultural Land Moderate 3 Settlement Low 2 Vegetation Very Low 1 6 NDVI Level -0.077-0.078 Very High 5 5.9 0.078–0.164 High 4 0.164–0.231 Moderate 3 0.231–0.294 Low 2 0.294–0.497 Very Low 1 7 Distance From River m 0-0.02 Very High 5 14.1 0.02–0.04 High 4 0.04–0.06 Moderate 3 0.06–0.08 Low 2 0.08–0.10 Very Low 1 8 Distance From Road m 0-0.03 Very High 5 5.6 0.03–0.06 High 4 0.06–0.09 Moderate 3 0.09–0.12 Low 2 0.12–0.14 Very Low 1 9 Drainage Density m 0-22.1 Very Low 1 9.3 22.11–44.2 Low 2 44.21–66.3 Moderate 3 66.31–88.41 High 4 88.41–110.5 Very High 5 10 Soil Type m Silty Loam Very Low 1 9.3 Silty Clay Low 2 Sandy Clay Moderate 3 Silty Clay High 4 Note : Here, the interpretation of rank is 1 = very low, 2 = low, 3 = moderate, 4 = high and 5 = very high. 2.7 Topographic wetness index (TWI) Beven and Kirkby (1979) established the Topographic Wetness Index (TWI), which combines upslope contributing area and local slope to assess spatial patterns of soil wetness. It has since become a popular input in hydrological modelling to evaluate soil saturation, enhance flood management techniques, and better understand terrain-driven hydrological processes [ 28 ]. TWI is particularly beneficial in identifying locations prone to surface saturation and floods [ 31 ]. The index is computed with the following formula: TWI = ln((𝐴𝑐𝑐|tan𝛽)) where Acc is the upslope contributing area, and β is the slope gradient. In this investigation, TWI values ranged from − 7.07 to 11.93. Using the natural breaks approach, these values were categorized into five flood susceptibility classes: very low (-7.07 to -3.34), low (-3.34 to -0.96), moderate (0.96 to 1.34), high (1.34 to 4.10), and very high (4.10 to 11.93) (Fig. 4 b). 2.8 Elevation Elevation has a significant impact on flood dynamics increasing the flood vulnerability of low-lying areas. Water naturally travels downslope due to gravity, causing runoff from elevated terrains such as hills or uplands to accumulate in low-lying places such as river valleys and flood plains. This gravity movement when coupled with insufficient or overburdened drainage infrastructure, significantly contributes to flooding in flat or low-lying regions [ 32 ]. A Digital Elevation Model (DEM) with a spatial resolution of 30 meters was used to create a thematic elevation map for this investigation. The elevation data was categorized using the natural breaks approach into five flood vulnerability zones: very high ( -23-3 m); high (3-6m); moderate ( 6-9m); low ( 9-12m); and very low ( 12-34m) (Fig. 4 c). 2.9 Precipitation Precipitation is the main cause of floods and waterlogs, with heavy rainfall in a short period of time being widely recognized as the dominant trigger (Tang et al., 2020). The amount of rainfall that ultimately contributes to river discharge depends to a large extent on the characteristics of the watershed, i.e. soil cover, slope and drainage capacity at the time of the event [ 28 ]. The data on precipitation for the period 2003 to 2023 were obtained from the data portal of the Centre for Hydrometeorology and Remote Sensing (CHRS). Multiple interpolation methods have been evaluated to improve spatial accuracy, with particular emphasis on the inverse weighted method using power parameters from 1 to 5 for the comparative analysis [ 30 ]. The data were converted to a raster layer with the required resolution and reclassified into five classes of flood influence: very low (271.23-277.89 mm); low (277.89-281.73 mm); moderate (281.73-285.54 mm); high (285.54–289.80 mm); and very high (289.80-294.62 mm) (Fig. 4 d). 2.10 Land use/land cover (LULC ) Land Use and Land Cover (LULC) is recognized as one of the most influential factors contributing to flood susceptibility, serving as a critical parameter in identifying areas at heightened risk of flooding [ 32 ]. LULC significantly affects hydrological processes by altering infiltration rates, surface runoff accumulation, surface–subsurface water interactions, and evapotranspiration dynamics[ 33 ]. Infiltration capacity varies greatly by land cover type; forested and vegetated regions often have high infiltration rates, whereas urbanized and stony surfaces tend to restrict water absorption, increasing the danger of flooding [ 34 ]. Land Use and Land Cover (LULC) mapping was conducted using Sentinel dat − 2A imagery obtained from the Copernicus Open Access Hub in 2023. The image was preprocessed, including geometric correction and resampling of the Level-2A result, to maintain uniform spatial resolution across all spectral bands. The imagery was subsequently mosaicked and spatially clipped to the study area using a shapefile and the Mask tool in ArcGIS 10.8 Desktop. The coordinate system was then converted to the geographic coordinate system, WGS84, from the Universal Transverse Mercator (UTM) projection. The Random Forest (RF) algorithm, a powerful ensemble learning technique that builds several decision trees and aggregates their outputs to increase classification accuracy and decrease overfitting, was used to classify LULC. This method improved the LULC map's overall accuracy and dependability [ 35 ]. The LULC map was reclassified into five classes: water body, Wetland, built-up, agriculture, and vegetation (Fig. 4 e). 2.11 Normalized difference vegetation index (NDVI) The Normalized Difference Vegetation Index (NDVI) is a widely recognized remote sensing indicator that is frequently employed in flood risk assessment due to its capacity to capture land surface characteristics and vegetation health [ 31 ]. NDVI effectively indicates vegetation cover, which significantly contributes to flood control by affecting infiltration and lowering surface runoff [ 36 ]. Its application is especially useful for identifying flood-prone locations, as sparse vegetation or impermeable surfaces are frequently associated with increased flood vulnerability [ 37 ]. The NDVI data used in this investigation were obtained from Landsat-8 satellite imagery. The index goes from − 1 to + 1, with higher values representing thick vegetation and lower values representing bare ground, built-up regions, or aquatic bodies. Within the study area, NDVI values ranged from − 0.077 to 0.497 (Fig. 4 f), indicating a wide range of land cover types and surface characteristics related to flood dynamics. The NDVI was determined using the following formula: NDVI = (𝑁𝐼𝑅−𝑅𝐸𝐷) / (𝑁𝐼𝑅+𝑅𝐸𝐷) where NIR and RED denote the near-infrared and red spectral regions of Sentinel-2A imagery, respectively. 2.12 Drainage density Drainage density is defined as the ratio of total stream length to total watershed area and is an important indication of surface runoff behavior. Higher drainage density is often associated with increased surface water flow and a higher risk of flood events [ 32 ]. In this study, drainage density was calculated using stream order data processed in ArcGIS 10.8. The Line Density function from the Spatial Analyst toolbox was used to calculate stream concentration throughout the watershed. The stream density map was divided into five categories based on natural breaks (Jenks) to indicate flood susceptibility levels: very low (0-22.1 km/km²), low (22.11–44.2 km/km²), moderate (44.21–66.3 km/km²), high (66.31–88.41 km/km²), and very high (88.41–110.5 km/km²) (Fig. 4 g). 2.13 Distance to rivers Proximity to streams is important in identifying flood susceptibility since it helps identify places that are more likely to flood[ 34 ]. Numerous studies have found that areas near rivers are more susceptible to floods due to overland flow accumulation [ 38 ]Areas farther from river networks, on the other hand, are less likely to experience floods [ 39 ]. Although there is no commonly accepted threshold distance for defining vulnerability, it is well understood that flood effect distances vary with river size. Small streams, for example, may only flood nearby areas, but huge rivers might damage areas several km distant [ 29 ]. In this study, a distance-to-streams map was created utilizing stream order data and ArcGIS 10.8's buffer analysis tool. The output was divided into five zones based on flood influence: very high (0-0.02 m), high (0.02–0.04 m), moderate (0.04–0.06 m), low (0.06–0.08 m), and very low (0.08–0.10 m) (Fig. 4 h). 2.14 Distance from Roads Distance from roads is considered a minor yet relevant factor influencing flood risk. Road networks often function as artificial barriers or embankments, affecting local water flow patterns and potentially obstructing or redirecting runoff. Consequently, there exists an inverse relationship between road proximity and flood susceptibility, with areas closer to roads generally experiencing lower flood risk due to elevated infrastructure or drainage planning. According to the Analytic Hierarchy Process (AHP) results, this factor ranked tenth in terms of relative importance among the variables considered in the flood susceptibility assessment [ 40 ]. Both large and minor roads are digitalized in GIS after being extracted from OpenStreetMap. Ultimately, a raster dataset is generated using the GIS tool for Euclidean distance. The identified distance to the study area’s road map is shown in Fig. 4 (i). 2.15 Soil Type Soil type is critical in regulating surface runoff and infiltration processes during rainstorm events [ 41 ]. It has a big impact on how much water infiltrates the earth vs becomes surface runoff. Soils with inadequate infiltration capacity retain more surface water, which increases the chance of flooding [ 31 ]. Soil data for Jashore District were gathered from the Bangladesh Agricultural Research Council's (BARC) soil site. The specified shapefile was rasterised for spatial analysis. The study region consists of four principal soil types: clay, sandy clay, silty clay, and silty loam. Based on the standard classification, these reflect very high, high, moderate, and low flood susceptibility[ 33 ](Fig. 4 j). 3. Results and Discussion 3.1 Assessment of flood susceptibility The flood susceptibility map was derived by overlaying the previously identified influencing factors, weighted using the Analytical Hierarchy Process (AHP) analysis, and utilizing the weighted overlay analysis feature of the ArcGIS platform. The resulting map was classified into five susceptibility zones: very low, low, moderate, high, and very high-risk areas. Figure 5 illustrates the flood susceptibility map. Table 4 provides the spatial distribution of the flood susceptibility zones, detailing their respective sizes and associated percentages of flood events. The flood susceptibility zones cover the following areas: 364.20 km² (very low), 1397.63 km² (low), 683.68 km² (moderate), 273.02 km² (high), and 83.68 km² (very high). These areas correspond to the following percentages of the total region: 12.99% (very low), 49.88% (low), 24.39% (moderate), 9.74% (high), and 2.99% (very high). Table 4 details the spatial distribution of these flood susceptibility classes within the study area. Table 4 Spatial distribution and area coverage of flood susceptibility classes Suitability class Area (km 2 ) Area (%) Very low 364.20 12.99 Low 1397.63 49.88 Moderate 683.68 24.39 High 273.02 9.74 Very high 83.68 2.99 The current study identifies the upazilas of Manirampur, Keshabpur, and Avoynagar as being at high and very high flood risk. Specifically, the Kapalia region of Manirampur and the Avoynagar region exhibit a particularly high flood risk. Conversely, the Sharsa upazila falls within the moderate risk category. The upper Chaugacha upazila, along with Bagherpara and Jhikargacha upazilas, are classified as very low or low-risk zones. The results of this study underline the significant impact of the topographic wetness index (TWI) on flood vulnerability. Areas with higher TWI in higher terrain slopes are associated with lower flood risk. The study shows a strong correlation between height and susceptibility zones, with higher heights generally associated with lower risk. Moreover, areas with gentle slopes tend to be less prone to flooding. Rainfall is the main driver of the increased risk in high-sensitive zones. Land use and land cover factors (LULCs) play a role in the dynamics of floods, but their overall weight in the final flood susceptibility model is limited because of the predominance of farmland in the area. Drainage density, although ranked lower in the AHP analysis than other factors, shows a significant impact, with areas with a higher drainage density generally having a lower flood risk. Moreover, regions with lower Normalized Difference Vegetation Index (NDVI) values are associated with an increased vulnerability to flooding. Although distance from rivers is an important factor, its impact on flood vulnerability is less pronounced in this study. The findings also highlight the significant impact of soil properties on the vulnerability of flood plains. Floodplain soil significantly increases flood susceptibility, while terraced landform areas present a lower flood risk. Although the overall impact of soil type is less pronounced in the flood vulnerability map, terraced land is particularly vulnerable to torrential rainfall. Several tactics can be used to lessen flooding. These include creating terraces to control and minimize surface runoff, planting trees to increase water retention, and establishing flood plains to redirect floodwaters. Additional engineering solutions to handle the excess water during floods include levees, dams, reservoirs, and retention ponds. Lastly, the study emphasizes how the El Nino Southern Oscillation (ENSO) and other global climatic events affect the frequency and severity of floods. El Navidad alters air circulation patterns, which often leads to unusual weather patterns like drought or heavy precipitation. In areas like Bangladesh, which are especially susceptible to monsoon storms, these impacts are especially significant. The necessity of incorporating global climate dynamics into local flood risk management measures is highlighted by the correlation between ENSO and flood susceptibility. The study highlights the interaction among various environmental and climatic factors offers a thorough understanding of flood vulnerability. The findings serve as a basis for the development of tailored mitigation strategies to reduce flood risks and boost resilience in susceptible places. 3.2 Discussion Floods take place every year in the south of the country, causing serious disruption and negative impacts on infrastructure, agriculture and human life, especially during the rainy season[ 8 ]. To reduce the detrimental consequences of flood events and assist effective disaster management initiatives, flood risk mapping, or FPM, is essential. This study shows that the Analytical Hierarchy Process (AHP) in conjunction with Geographic Information System (GIS) and Remote Sensing (RS) technology provides an effective approach for identifying flood-prone locations[ 34 ]. The study precisely identified regions in the Jashore district with varying flood susceptibility using ten flood-conditioning criteria, and it offered a solid framework for upcoming flood risk mitigation and management initiatives. Because of their immediate impact on water flows and buildup, previous research has shown that elevation, slope, land use and land cover (LULC), and distance from streams are some of the most significant causes of floods (Tehrany et al., 2019; [ 32 ]. These factors were also found to be crucial in determining the Jashore area's flood susceptibility. A thorough study was ensured by include ten elements, which is corroborated by earlier studies that indicate additional characteristics improve the reliability of FSM, especially in local studies with complex topography and changing hydrology (Md. Islam & Shahriar, 2023). The latest FSM divided the Jashore region into five flood risk zones: very low, low, medium, high, and very high. The findings revealed that the very low and low sensitivity zones were primarily found in the district's higher peaks and northern sections, whereas the moderate sensitivity zones were centered in the district's middle areas. In contrast, the south and south-east regions, which are characterized by low-lying plains and proximity to watercourses, are highly to extremely vulnerable to flooding. These findings are consistent with earlier investigations [ 8 ], which highlight the susceptibility of flat, low-elevation areas near rivers and streams to regular floods. 3.3 Validation The assessment of the developed flood susceptibility map for Jashore District involved a thorough evaluation of its reliability and practical applicability, utilizing a mix of internal consistency checks and qualitative validation in relation to established regional flood characteristics. This study introduces a comprehensive AHP-based flood susceptibility model for the area, but a traditional external validation using extensive, independently collected historical flood inventory data was not feasible. Nonetheless, the Analytical Hierarchy Process (AHP) incorporates an essential self-consistency measure. The computed Consistency Ratio (CR) for the pairwise comparison matrix was 0.37%, which is well below the 10% threshold, demonstrating a strong level of internal consistency and reliability of the assigned factor weights. Moreover, the spatial arrangement of the identified susceptibility zones closely corresponds with local insights and earlier studies regarding flood patterns in Jashore. High and very high-risk areas were precisely identified in low-lying plains marked by significant rainfall and closeness to rivers, including Kapalia, Manirampur, Avoynagar, and Keshabpur Upazila. In contrast, areas identified as low-risk were associated with elevated altitudes and steeper slopes, such as the uplands of Chowgacha and Jhikargacha. The qualitative alignment with established flood behavior in the region strengthens the confidence in the model's output and its effectiveness as a foundational tool for managing flood risk. 3.4 Model Limitations and Prospects for Advancement Although the AHP method has demonstrated effectiveness in FSM, some limits must be acknowledged. The inclusion of subjective expert assessment in a pairwise comparative matrix (PCM) can generate bias and inconsistencies. Furthermore, data availability and quality continue to be significant issues, particularly in locations where there have been substantial shifts in climate and environmental factors in recent decades. To address these constraints, advanced techniques such as fuzzy AHP, Artificial Neural Network (ANN), Linear Regression Models, and Hybrid Machine Learning Models could improve FSM reliability and accuracy. The study provides useful information for managing flood hazards in the Jashore area, as well as a trustworthy flood risk map that policymakers, urban planners, and local governments may use. The findings are used to inform land-use planning, infrastructure development, and disaster-preparedness measures aimed at reducing the harmful effects of future floods. Furthermore, this research helps to establish sustainable flood management practices and long-term policies to strengthen the Jashore area's flood resistance. 4. Conclusion An adequate flood and water-logging risk assessment approach is essential for developing resilience communities in the face of climate change and urbanization. This study used a GIS-based Analytical Hierarchy Process (AHP) to Evaluate the spatial susceptibility to flooding and waterlogging in the Jashore district, incorporating 10 major criteria such as topographic wetness index (13.8%), precipitation (13.5%), elevation (12.1%), and proximity to rivers (14.1%). Based on weighted factor analysis, the region was divided into five susceptibility zones ranging from very low to very high, with high-risk zones located in flat, low-lying areas near rivers with inadequate drainage, both of which contribute to flooding and water logging. In contrast, low-risk locations have higher altitudes, steeper slopes, and land uses that are less susceptible to saturation. While the GIS-AHP framework was useful, it had limitations, such as the removal of subsurface drainage infrastructure and dynamic soil moisture characteristics, both of which are essential for evaluating waterlogging. Furthermore, utilizing specialist expertise for weight distribution adds subjectivity. Future studies should use hybrid models that incorporate expert insights with machine learning or hydrological simulation to increase accuracy and reproducibility. Despite these limitations, the findings provide a consistent framework for spatial flood analysis and water logging risk, which can help with disaster risk reduction, urban drainage planning, and adaptive land use strategies in Jashore and other flood-prone areas. Declarations Acknowledgements The authors extend their sincere gratitude to the Jashore Municipality for its support and cooperation in facilitating this study. We also acknowledge the United States Geological Survey (USGS) and Esri for providing free access to satellite imagery, the Bangladesh Agricultural Research Council (BARC) for supplying soil shapefiles, the Center for Hydrometeorology and Remote Sensing (CHRS) data portal for rainfall shapefiles, and OpenStreetMap for shapefiles related to river and road networks. All authors have read, understood, and, where applicable, complied with the ethical responsibilities outlined in the "Instructions for Authors". Author contributions MRH: Conceptualization; methodology; formal analysis; mapping original draft; review; and editing; MIH: Formal analysis; methodology; review, supervision and editing; ZHS: Mapping methodology; review; and editing. Funding This study did not receive any funding. Data availability Data and materials used to present the study findings are available upon a reasonable request. Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. References C. Faiz Ahmed and N. Kranthi, “Flood Vulnerability Assessment using Geospatial Techniques: Chennai, India,” Indian J Sci Technol , vol. 11, no. 6, pp. 1–13, Feb. 2018, doi: 10.17485/IJST/2018/V11I6/110831. N. Islam and K. Dharanirajan, “ Flood impact assessment in Murshidabad District of West Bengal using remote sensing and GIS. ,” Int J Adv Remote Sens GIS , pp. 48–57, 2017. N. Logesh, T. Usha, G. Muthusankar, P. K. Bavinaya, M. Prabhakaran, and C. Lakshumanan, “Flood susceptibility analysis in Chennai Corporation using Frequency Ratio Model,” Journal of the Indian Society of Remote Sensing , vol. 49, pp. 1533–1543, 2021, Accessed: Jan. 19, 2025. [Online]. Available: https://hal.science/hal-03157379 B. M. R. Faisal and Y. S. Hayakawa, “Geomorphological processes and their connectivity in hillslope, fluvial, and coastal areas in Bangladesh: A review,” Progress in Earth and Planetary Science 2022 9:1 , vol. 9, no. 1, pp. 1–22, Jul. 2022, doi: 10.1186/S40645-022-00500-8. M. Becker et al. , “Coastal Flooding in Asian Megadeltas: Recent Advances, Persistent Challenges, and Call for Actions Amidst Local and Global Changes,” Reviews of Geophysics , vol. 62, no. 4, p. e2024RG000846, Dec. 2024, doi: 10.1029/2024RG000846. Ullah. M. Shariot, “Sea-level rise and sustainable shore protection strategies in the low-lying delta: A case study of Bangladesh,” Reg Stud Mar Sci , vol. 71, Apr. 2024, doi: 10.1016/J.RSMA.2024.103424. F. F. and W. C. of B. FFWC and W. D. B. BWDB, “Annual Flood Report,” 2016. Accessed: Jan. 19, 2025. [Online]. Available: http://www.ffwc.gov.bd/images/annual16.pdf M. S. Khan and M. A. Hossan, “Water-logging and its’ impacts on livelihood: A study from Jashore Sadar Upazila, Jashore, Bangladesh,” Journal of Tropical Resources and Sustainable Science (JTRSS) , vol. 9, no. 1, pp. 58–66, Aug. 2021, doi: 10.47253/JTRSS.V9I1.711. Z. Ahmed and S. Ambinakudige, “Does land use change, waterlogging, and salinity impact on sustainability of agriculture and food security? Evidence from southwestern coastal region of Bangladesh,” Environ Monit Assess , vol. 195, no. 1, pp. 1–28, Jan. 2023, doi: 10.1007/S10661-022-10673-W/FIGURES/25. D. S. Bisht, C. Chatterjee, S. Kalakoti, P. Upadhyay, M. Sahoo, and A. Panda, “Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study,” Natural Hazards , vol. 84, no. 2, pp. 749–776, Nov. 2016, doi: 10.1007/S11069-016-2455-1/FIGURES/17. S. E. Haque and N. Nahar, “Bangladesh: Climate Change Issues, Mitigation, and Adaptation in the Water Sector,” ACS ES and T Water , vol. 3, no. 6, pp. 1484–1501, Jun. 2023, doi: 10.1021/ACSESTWATER.2C00450/ASSET/IMAGES/MEDIUM/EW2C00450_0005.GIF. M. S. Tehrany, B. Pradhan, and M. N. Jebur, “Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method,” Stochastic Environmental Research and Risk Assessment , vol. 29, no. 4, pp. 1149–1165, May 2015, doi: 10.1007/S00477-015-1021-9/FIGURES/9. F. Pappenberger, P. Matgen, K. J. Beven, J. B. Henry, L. Pfister, and P. Fraipont, “Influence of uncertain boundary conditions and model structure on flood inundation predictions,” Adv Water Resour , vol. 29, no. 10, pp. 1430–1449, Oct. 2006, doi: 10.1016/J.ADVWATRES.2005.11.012. O. Rahmati et al. , “Development of novel hybridized models for urban flood susceptibility mapping,” Scientific Reports 2020 10:1 , vol. 10, no. 1, pp. 1–19, Jul. 2020, doi: 10.1038/s41598-020-69703-7. H. Mojaddadi, B. Pradhan, H. Nampak, N. Ahmad, and A. H. bin Ghazali, “Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS,” Geomatics, Natural Hazards and Risk , vol. 8, no. 2, pp. 1080–1102, Dec. 2017, doi: 10.1080/19475705.2017.1294113. M. Rahman et al. , “Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh,” J Environ Manage , vol. 295, p. 113086, Oct. 2021, doi: 10.1016/J.JENVMAN.2021.113086. B. T. Pham et al. , “Flood risk assessment using deep learning integrated with multi-criteria decision analysis,” Knowl Based Syst , vol. 219, p. 106899, May 2021, doi: 10.1016/J.KNOSYS.2021.106899. C. Luu, J. von Meding, and M. Mojtahedi, “Analyzing Vietnam’s national disaster loss database for flood risk assessment using multiple linear regression-TOPSIS,” International Journal of Disaster Risk Reduction , vol. 40, p. 101153, Nov. 2019, doi: 10.1016/J.IJDRR.2019.101153. A. M. Al-Abadi, A. A. Al-Temmeme, and M. A. Al-Ghanimy, “A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq,” Sustain Water Resour Manag , vol. 2, no. 3, pp. 265–283, Sep. 2016, doi: 10.1007/S40899-016-0056-5/FIGURES/15. D. S. Fernández and M. A. Lutz, “Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis,” Eng Geol , vol. 111, no. 1–4, pp. 90–98, Feb. 2010, doi: 10.1016/J.ENGGEO.2009.12.006. Md. T. Islam and Md. A. Shahriar, “Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques,” Oct. 2023, doi: 10.21203/RS.3.RS-3492845/V1. D. Bosch, “Final Proof,” Ann Intern Med , vol. 156, no. 12, p. 900, 2012, doi: 10.7326/0003-4819-156-12-201206190-00015. M. S. Shadmaan and K. M. Hassan, “Assessment of flood susceptibility in Sylhet using analytical hierarchy process and geospatial technique,” Geomatica , vol. 76, no. 1, p. 100003, 2024, doi: 10.1016/j.geomat.2024.100003. T. L. Saaty, “How to make a decision: The analytic hierarchy process,” Eur J Oper Res , vol. 48, no. 1, pp. 9–26, Sep. 1990, doi: 10.1016/0377-2217(90)90057-I. Z. Kader, R. Islam, T. Aziz, M. Hossain, R. Islam, and M. Miah, “GIS and AHP ‑ based flood susceptibility mapping : a case study of Bangladesh,” Sustain Water Resour Manag , vol. 10, no. 5, pp. 1–18, 2024, doi: 10.1007/s40899-024-01150-y. H. Akay, “Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods,” Soft comput , vol. 25, no. 14, pp. 9325–9346, 2021, doi: 10.1007/s00500-021-05903-1. L. Gigović, D. Pamučar, Z. Bajić, and S. Drobnjak, “Application of GIS-interval rough AHP methodology for flood hazard mapping in Urban areas,” Water (Switzerland) , vol. 9, no. 6, pp. 1–26, 2017, doi: 10.3390/w9060360. M. S. Tehrany, L. Kumar, and F. Shabani, “A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia,” PeerJ , vol. 2019, no. 10, p. e7653, Oct. 2019, doi: 10.7717/PEERJ.7653/SUPP-1. R. U. Zzaman, S. Nowreen, M. Billah, and A. S. Islam, “Flood hazard mapping of Sangu River basin in Bangladesh using multi-criteria analysis of hydro-geomorphological factors,” J Flood Risk Manag , vol. 14, no. 3, p. e12715, Sep. 2021, doi: 10.1111/JFR3.12715. S. E. Tabarestani and H. Afzalimehr, “A comparative assessment of multi-criteria decision analysis for flood susceptibility modelling,” Geocarto Int , vol. 37, no. 20, pp. 5851–5874, Oct. 2022, doi: 10.1080/10106049.2021.1923834. X. Tang, J. Li, M. Liu, W. Liu, and H. Hong, “Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods,” Catena (Amst) , vol. 190, p. 104536, Jul. 2020, doi: 10.1016/J.CATENA.2020.104536. K. Ullah and J. Zhang, “GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan,” PLoS One , vol. 15, no. 3, p. e0229153, 2020, doi: 10.1371/JOURNAL.PONE.0229153. H. Allafta and C. Opp, “GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran,” Geomatics, Natural Hazards and Risk , vol. 12, no. 1, pp. 2087–2116, 2021, doi: 10.1080/19475705.2021.1955755. Lappas I and Kallioras A, “Flood Susceptibility Assessment through GIS-Based Multi-Criteria Approach and Analytical Hierarchy Process (AHP) in a River Basin in Central Greece,” International Research Journal of Engineering and Technology , vol. 738, 2008, Accessed: Jan. 26, 2025. [Online]. Available: www.irjet.net M. Pal, “Random forest classifier for remote sensing classification,” Int J Remote Sens , vol. 26, no. 1, pp. 217–222, Jan. 2005, doi: 10.1080/01431160412331269698. A. Saha et al. , “Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms,” Water 2021, Vol. 13, Page 241 , vol. 13, no. 2, p. 241, Jan. 2021, doi: 10.3390/W13020241. I. Chowdhuri, S. C. Pal, and R. Chakrabortty, “Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India,” Advances in Space Research , vol. 65, no. 5, pp. 1466–1489, Mar. 2020, doi: 10.1016/J.ASR.2019.12.003. S. Das and A. Gupta, “Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India,” Geoscience Frontiers , vol. 12, no. 5, p. 101206, Sep. 2021, doi: 10.1016/J.GSF.2021.101206. S. Rehman, M. Sahana, H. Hong, H. Sajjad, and B. Bin Ahmed, “A systematic review on approaches and methods used for flood vulnerability assessment: framework for future research,” Natural Hazards , vol. 96, no. 2, pp. 975–998, Mar. 2019, doi: 10.1007/S11069-018-03567-Z/FIGURES/7. M. Hussain, M. M. Ajmal, M. Khan, and H. Saber, “Competitive priorities and knowledge management: An empirical investigation of manufacturing companies in UAE,” Journal of Manufacturing Technology Management , vol. 26, no. 6, pp. 791–806, 2015, doi: 10.1108/JMTM-03-2014-0020. T. Islam et al. , “Flood susceptibility modelling using advanced ensemble machine learning models,” Geoscience Frontiers , vol. 12, no. 3, p. 101075, May 2021, doi: 10.1016/J.GSF.2020.09.006. Md. Islam and Md. A. Shahriar, “Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques,” Oct. 2023, doi: 10.21203/RS.3.RS-3492845/V1. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7106425","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504711338,"identity":"2f4e6e53-35ee-47ea-b34a-226e08ac3b33","order_by":0,"name":"Md Refath 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area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7106425/v1/63d2fb0db1c662b80320fb12.png"},{"id":89830624,"identity":"8910621d-f1f1-4f02-8e8f-6ca4a4113ef0","added_by":"auto","created_at":"2025-08-25 13:26:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":248657,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological framework for flood risk assessment\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7106425/v1/51e95d11872021327b876706.png"},{"id":89829370,"identity":"80e032eb-8d76-4839-a831-9489a70cd5a0","added_by":"auto","created_at":"2025-08-25 13:10:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360805,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise comparison matrix and normalized principal eigenvector for flood risk assessment factors\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7106425/v1/54b9d1bf755115aa7d784ddf.png"},{"id":89829598,"identity":"161c783a-07ab-4629-b54a-8fd088b8cb75","added_by":"auto","created_at":"2025-08-25 13:18:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2172412,"visible":true,"origin":"","legend":"\u003cp\u003eThematic maps of the study area (a) slope, (b) topographic wetness index (TWI), (c) elevation, (d) Rainfall, (e) LULC, (f) NDVI, (g) drainage density, (h) distance from rivers, (i) distance from roads and (j) soil texture.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7106425/v1/224d1c0df2e6a6abcd065b4f.png"},{"id":89830625,"identity":"016a35d5-0d84-4a1e-ae64-3b81bda39a52","added_by":"auto","created_at":"2025-08-25 13:26:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1402205,"visible":true,"origin":"","legend":"\u003cp\u003eFlood susceptibility map of Jashore District.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7106425/v1/ef6e69942995de286468611a.png"},{"id":91899659,"identity":"ac816fe4-8be8-4980-9bc5-69beeec4524c","added_by":"auto","created_at":"2025-09-22 20:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6503617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7106425/v1/da888721-a7b8-4761-85b8-f31f60f00130.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Flood and Water Logging Risk Assessment of Jashore District Leveraging the Analytical Hierarchy Process (AHP) Through a Spatial Analysis Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFloods are one of the most devastating natural disasters, causing widespread damage to communications networks, infrastructure and property. They seriously affect agricultural output, land, and other vital resources, and they result in large losses of human and animal life. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The primary cause of these occurrences is abrupt, extensive, and recurring precipitation, particularly in tropical and subtropical regions where the monsoon is present [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Floods are common in Bangladesh, where June through September receives more than 80% of the country's yearly rainfall (Uddin \u0026amp; Matin, 2021). Flooding is made worse by this intense monsoon rains, tidal surges, cyclonic occurrences, upstream runoff, and poor drainage systems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]Over the years, these occurrences have continuously caused significant harm to flood plains, deltas, and coastal regions around the nation. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Bangladesh is especially susceptible to frequent flood disasters because of its primarily low-lying terrain [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Bangladesh is still especially susceptible to frequent flood disasters because of its primarily low-lying terrain [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This is also true in Bangladesh's southwest, particularly the Jashore District. It is especially susceptible to flooding and chronic water scarcity due to its geographic location, shifting climatic patterns, and human activities. According to historical records, there were large flooding incidents in 1988, 1998, 2004, 2007, and 2014 all of which resulted in severe infrastructure damage and deaths [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The Jashore region's floods are largely driven by runoff build-up in upstream areas quickly and the downstream excess water releasing slowly [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Periods of heavy and protracted precipitation often exacerbate these conditions, resulting in a complicated web of hydrological elements that raises the danger of flooding. When these mechanisms function in tandem, water levels frequently rise sharply before falling quickly [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Floods are made more severe by the inadequacy of the drainage network and the major rivers' inability to handle excessive water flows [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdopting comprehensive flood management strategies and mitigation techniques is necessary to address these issues and reduce the negative effects on ecosystems, agriculture, and infrastructure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, identifying areas prone to flooding is essential for creating focused treatments and efficient early warning systems. In order to strengthen resilience and shield vulnerable areas from the growing risks of floods, several proactive steps are required [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Floods are the result of multifactorial and dynamic processes, which make their prevention almost impossible [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a result, minimizing the overwhelming effects of floods has become one of the most urgent global challenges [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Effective flood risk mitigation depends on rapid and precise risk evaluations, which play a key role in developing robust and actionable risk management strategies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Implementation of advanced geospatial observation and GIS technology has transformed flood risk assessment, giving academics and practitioners significant tools for more accurate and efficient risk assessment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Flood risks are evaluated by analyzing spatially susceptible regions that are particularly vulnerable to floods and where the potential for devastating consequences is greatly raised [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A comprehensive flood risk assessment is necessary for an efficient flood risk management system since it involves precise estimation of flood hazards, exposure, and susceptibility [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Flood risk assessment now includes geospatial technology like Remote Sensing (RS) and Geographic Information Systems (GIS). They are indispensable for enhancing the accuracy of flood vulnerability assessments and management because of their exceptional precision and accessibility due to their sophisticated skills in data integration, geographical analysis, and visualization (Luu et al., 2019;Tehrany et al., 2015). The analytical hierarchy process (AHP), a popular multi-criteria decision-making technique (MCDM), serves a fundamental function in recognizing and assessing the primary contributors to flood risk. The AHP simplifies the calculation of a comprehensive flood risk index by assigning weights and categories to many elements [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. AHP's ability to tackle complicated decision-making problems, even with insufficient data, makes it a useful tool for mapping flood vulnerability (Fern\u0026aacute;ndez \u0026amp; Lutz, 2010; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn order to overcome these drawbacks, the paper presents a novel method for mapping flood susceptibility in Bangladesh's southeast and Jashore coastal regions by combining AHP with GIS. The flood risk index, which considers a complicated collection of factors such elevation, topographic wetness index, slope, drainage density, rainfall, distance from the road, closeness to the river, and soil properties, is calculated using a pairwise comparison approach. This advancement contributes meaningfully to the enhancement of environmental management, and socioeconomic resilience while offering a strong framework for mapping flood susceptibility. The findings of this study will be of great use to researchers, policymakers, and flood-management specialists, opening the door to more effective and comprehensive and durable flood resilience planning in Bangladesh's southern and Jashore coastal areas.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area\u003c/h2\u003e\u003cp\u003eThe survey was conducted in the Jashpur area of southwestern Bangladesh. The district is bordered to the north by the Jhenaidah River and to the west by the West Bengal State. Jashore, which occupies 2,606.94 km2, is home to an estimated 2,764,547 people. It is administratively divided into 8 upazilas (subdivisions) and 92 union districts, located at 22.47 to 23.47 degrees north latitude and 88.40 to 89.50 degrees east longitude [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Jashore is an integral part of the vast Ganges delta, which is known for its fertile alluvial deposits, which are the result of the downstream processes of sedimentation in the Ganges-Brahmaputra-Meghna basin. The district is crossed by important rivers such as the Bhairava, the Kapotaksha and the Ichamati, which are all siltated and combined with poor drainage infrastructure have exacerbated water scarcity. These factors, combined with heavy rainfall from the monsoon, contribute significantly to recurrent flooding and long-term water scarcity, which affects livelihoods and agricultural activity. The area is characterised by a tropical monsoon climate, with most precipitation occurring between June and September. Average annual rainfall is approximately 1,800 mm. Temperatures range from a balmy 43 degrees Celsius in April, the hottest month, to a chilly 7 degrees Celsius in January, the coldest month (Khan, 2012). The predominant land use in Jashore is agriculture, with almost 62.5 percent of the population engaged in agricultural activity. Rice, cotton and vegetables are the backbone of the local economy, but they are very vulnerable to the negative effects of flooding and droughts. In addition to crop production, fish farming is an increasingly important source of livelihood in the region. The extensive river systems and ponds of the Jashore area provide favorable conditions for aquaculture and offer an alternative source of income for the local population. However, the same problems of waterlogging and flooding also affect fish farming activities, leading to fish losses and affecting the economic stability of aquaculture users.\u003c/p\u003e\u003cp\u003eThe Jashore area's topographical and hydrological characteristics make it especially prone to floods due to a mix of causes such as poor drainage systems, river slippage, and overflow from upstream sources during the monsoon season. Recurrent waterlogging not only affects agricultural land, but it also causes significant socioeconomic problems in the sedimentary basin. Major floods and landslides in recent years have resulted in considerable crop and fish farm losses, highlighting the region's vulnerability and the urgent need for long-term flood mitigation solutions. These criteria support the Jashore area's selection as a study site for this research, which intends to provide information on waterlogging reduction, regional resilience development, and agricultural and aquaculture productivity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection and Acquisition\u003c/h2\u003e\u003cp\u003eThe collection and acquisition of data is critical in the early stages of a research approach. Comprehensive data sets will be created, including elevation, land use and area, slope, average precipitation, drainage density, distance from roads, proximity to rivers, topographic vegetation index (TOPI), normalized difference vegetation index (NDVI), and soil structure. These data sets will form a multidimensional foundation for conducting detailed geographical and environmental investigations. By combining these various factors, this phase provides a firm foundation for a scientifically sound investigation, ensuring methodological accuracy and increasing the overall legitimacy and usefulness of the research findings. This phase covers the collection of high-resolution satellite photos of the Jashore area to serve as the foundation for geospatial analysis. These images serve as the foundation for precise mapping and study of the battlefield's features. In addition, it is essential to obtain all necessary licenses and authorizations for the use of proprietary geospatial data. Compliance with these protocols not only upholds ethical standards of research, but also ensures compliance with intellectual property laws, thereby enhancing the integrity and credibility of the study.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeospatial and Environmental data sources for flood risk assessment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl.no\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData Sources\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u0026times;30, 1 Arc 30m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUS Geological Survey (USGS) Shuttle Radar Topography\u003c/p\u003e\u003cp\u003eMission (SRTM) data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevation, slope, TWI,\u003c/p\u003e\u003cp\u003edrainage density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u0026times;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGenerated from the DEM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u0026times;10, Raster Image Environmental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSystems Research Institute (ESRI)\u003c/p\u003e\u003cp\u003eSentinel-2data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil texture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShape File\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBangladesh Agricultural Research Council (BARC)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 Years average Rainfall (Shape File)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCHRS data portal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to rivers,\u003c/p\u003e\u003cp\u003edistance to roads\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShape File\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBBBike extracts OpenStreetMap\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Preprocessing\u003c/h2\u003e\u003cp\u003eFollowing the acquisition of the data, the next step is data preparation, to prepare the data for analysis. It is important to eliminate any errors and inconsistencies in the datasets obtained in order to ensure the quality and accuracy of the data. Subsequently, different data sets will be integrated, including elevation, TWI, slope, LULC, NDVI, rainfall, drainage density, distance from roads, distance from rivers and soil structure. All data sets will be continuously projected and geo-referenced to the common spatial analysis coordinate system\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Application of Analytical Hierarchy Process (AHP)\u003c/h2\u003e\u003cp\u003eOne of the unique aspects of the science of flood and water management risk assessment is the creation of a number of conceptual frameworks which define different indicators and thus represent a discrete process of assessing vulnerability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The present investigation is based on a methodology combining the AHP and the GIS. The RS data are collected and processed by the ArcGIS platform for the purpose of mapping flood vulnerability. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the methodological steps, including data collection, data processing and profiling. The compilation of maps for several flood-related factors is the first step in the process of flood susceptibility mapping. The study used ten factors to influence flooding: dem, slope, drainage density, soil type, rainfall, topographic moisture index (TMI), land use and area (LULC), river distance and road distance. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the source of the collected data. After obtaining information from several sources, the GIS environment is used to process the collected maps and to create a topic map for each factor. ArcGIS software 10.8 is used to process data, analyses them and develop thematic layers in relation to the above-mentioned impact factors. A thematic map of different features is used to define sub-classes according to the impact of these aspects on flood vulnerability.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is an example of a table and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is an example of a table legend. The Analytical Hierarchical Process (AHP) is one of the most popular and successful multi-criteria decision analysis techniques (MCDA). Following the creation of thematic maps for the influencing factors, the AHP is used to weight the sub-categories of the factors. AHP helps decision makers to break complex issues down into their hierarchical structure by methodically identifying the essentials, such as goals, standards, and options. This method provides a robust framework for informed and logical decision making in research and analysis by facilitating prioritisation of components and their relationship[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the weights that were obtained from the primary eigenvector of the pairwise correlation matrix. A systematic method for quantitatively comparing decision criteria is a pairwise comparison matrix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the normalized pairwise comparison matrix and associated weights that were determined by the approximation method. With a weight of 0.14, distance from river was the most important factor; distance from river was the least important at 0.05. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e gives details on the other weights for slope, TWI, elevation, precipitation, LULC, NDVI, drainage density and soil structure, while the plot below illustrates the latter. deductive remittance. The weighted-average maps were made of the elevation, slope, TWI, LULC, NDVI, drainage density, rainfall, distance from the river, distance from the roads, and soil structure. These maps were used as a starting point for further research. The AHP approach was used to make pairwise comparisons to determine the relative importance of the different components.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eScale for Degree of Significance in Pairwise Comparison (AHP) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegree of Significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition (Preferences on a Scale)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription (Explanation)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqually significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe two criteria have the same importance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate significance of one over another\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne criterion is moderately superior to another based on experience and judgment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCritical or strong significance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne criterion is essentially or strongly superior to another based on experience and judgment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery strong significance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne criterion is very strongly superior to another based on experience and judgment. And its power is reflected in practice.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtremely significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne criterion is extremely superior to another based on experience and judgment. And its power is reflected in practice. The evidence is of the greatest confirmation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2, 4, 6, 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate significance between two adjacent preferences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUsed to indicate the compromises between the above judgments (i.e., 1, 3, 5, 7, and 9).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNumerous studies around the world use this ranking approach to rank elements according to their importance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The parallel comparison matrix determines the weight or priority of the components in the AHP descriptor. According to Saaty, a linear scale of 1 to 9 is used to assign a score to create a randomized comparative matrix. According to the Saaty linear scale, 1 indicates equal importance, 9 indicates significantly more importance and 1 or 9 indicates extremely less importance. Protestant martyrdom. 4 lists the importance of each score. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a pairwise matrix of several criteria. The priority of each factor is determined on the basis of the importance found in the pairwise comparison matrix (i.e. the AHP analysis). According to the Saaty approach, the optimal pairwise comparison matrix should be less than 10percent. The consistency index (CI) has been divided by the random index (RI) in Eq.\u0026nbsp;(1) to determine the value of CR.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}CR=\\frac{CI}{RI}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe consistency ratio is represented by CR, the consistency index by CI, and the random index by RI, the value of which depends on how many parameters have been compared. The Eq.\u0026nbsp;(2) was used to get the consistency index (CI)\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}CI=\\frac{{\\lambda\\:}_{max}-n}{n-1}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe consistency index (CI) is calculated from the average eigenvalue (k) of the consistency vector, with n being the total number of thematic layers included in the analysis. According to the analytical hierarchy principle, the coefficient of variation (CR) should not exceed 10 percent to ensure the reliability of the weights derived (Saaty, 1990). The calculated CR in this study is 0.37 percent, which indicates a high level of consistency and reliability in the rating process. For the compilation of the composite flood vulnerability map, each of the class factor maps was multiplied by its respective weight using the weighted sum method. The weighted maps were then added together as shown in Eq.\u0026nbsp;3 to form the final composite map.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}FS=\\sum\\:Wi\\times\\:Xi\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere FS stands for flood susceptibility, Wi for factor i\u0026rsquo;s weight, and Xi for each factor i\u0026rsquo;s classes of susceptibility. Next, weight is allocated to each factor\u0026rsquo;s subcategories according to its ranking priority, as shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In order to create the flood susceptibility map, the weighted overlay analysis of the components is then carried out on the ArcGIS platform using the provided weighting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Geospatial Analysis\u003c/h2\u003e\u003cp\u003eThe vulnerability to floods in the study area will be systematically assessed through a framework of multi-criteria geospatial analysis, integrating spatial data sets with weighted factors derived by the analysis hierarchy process. This methodological approach allows quantitative and reproducible flood risk assessments by synthesizing a variety of environmental and anthropogenic variables. The basic analytical technique used in this process will be overlay analysis, which makes it possible to superimpose several thematic layers to determine spatial patterns of flood vulnerability. The geospatial data sets to be integrated include Sentinel-2A high resolution images, digital elevation models (DEM), land use and land cover (LULC), slope gradients, rainfall intensity, drainage densities and proximity indices such as road and river distances. These data sets will be standardized and integrated using spatial modeling techniques based on the doctrine of sovereignty. The resulting comprehensive flood risk maps will reveal spatial diversity and complex interactions between contributing factors, and provide a detailed representation of flood risk areas. This integrated approach, combining empirical data, expert weighting of AHP and advanced spatial analysis, will culminate in the development of flood risk maps for the Jordon area. Each defined area will reflect a specific level of vulnerability and will support evidence-based planning and targeted disaster risk reduction strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Slope\u003c/h2\u003e\u003cp\u003eSlope is an important topographic feature that influences flood behavior since it directly impacts surface runoff velocity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Steep slopes promote faster water circulation and limit infiltration, making such regions less prone to floods [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Flat or moderately sloping terrains, on the other hand, retain more runoff, increasing the likelihood of floods caused by water accumulation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The slope layer was produced from the Digital Elevation Model (DEM) using ArcGIS surface analysis tools. The slope values were then divided into five flood susceptibility classes using natural breaks: very high (0-1.13%), high (1.13\u0026ndash;2.06%), moderate (2.06\u0026ndash;3.20%), low (3.20\u0026ndash;4.85%), and very low (5.85\u0026ndash;26.35%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Each class was given a score ranging from 1 to 5 to describe its relative influence on flooding, with 5 (extremely low slope) indicating the most flood risk and 1 (very high slope) indicating the lowest.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeights assigned to thematic layers for flood risk assessment.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlood Causative Criterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSusceptibility Class Ratings and Ranges\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSusceptibility Class Rating\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWeight %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTopographic Wetness Index (TWI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.07 -3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.34 -0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96\u0026ndash;1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u0026ndash;4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.10-11.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-23-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e12.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13\u0026ndash;2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.06\u0026ndash;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.20\u0026ndash;4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.85\u0026ndash;26.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003ePrecipitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003emm/20 years Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27123\u0026ndash;27789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27789\u0026ndash;28173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28173\u0026ndash;28554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28554\u0026ndash;28980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28980\u0026ndash;29462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWater Body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgricultural Land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSettlement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.077-0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.078\u0026ndash;0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.164\u0026ndash;0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.231\u0026ndash;0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.294\u0026ndash;0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDistance From River\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e14.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u0026ndash;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u0026ndash;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026ndash;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026ndash;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDistance From Road\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u0026ndash;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026ndash;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u0026ndash;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u0026ndash;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDrainage Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.11\u0026ndash;44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.21\u0026ndash;66.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.31\u0026ndash;88.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.41\u0026ndash;110.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSoil Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSilty Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSilty Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSandy Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSilty Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: Here, the interpretation of rank is 1\u0026thinsp;=\u0026thinsp;very low, 2\u0026thinsp;=\u0026thinsp;low, 3\u0026thinsp;=\u0026thinsp;moderate, 4\u0026thinsp;=\u0026thinsp;high and 5\u0026thinsp;=\u0026thinsp;very high.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Topographic wetness index (TWI)\u003c/h2\u003e\u003cp\u003eBeven and Kirkby (1979) established the Topographic Wetness Index (TWI), which combines upslope contributing area and local slope to assess spatial patterns of soil wetness. It has since become a popular input in hydrological modelling to evaluate soil saturation, enhance flood management techniques, and better understand terrain-driven hydrological processes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. TWI is particularly beneficial in identifying locations prone to surface saturation and floods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The index is computed with the following formula:\u003c/p\u003e\u003cp\u003eTWI\u0026thinsp;=\u0026thinsp;ln((\u0026#119860;\u0026#119888;\u0026#119888;|tan\u0026#120573;))\u003c/p\u003e\u003cp\u003ewhere Acc is the upslope contributing area, and β is the slope gradient. In this investigation, TWI values ranged from \u0026minus;\u0026thinsp;7.07 to 11.93. Using the natural breaks approach, these values were categorized into five flood susceptibility classes: very low (-7.07 to -3.34), low (-3.34 to -0.96), moderate (0.96 to 1.34), high (1.34 to 4.10), and very high (4.10 to 11.93) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Elevation\u003c/h2\u003e\u003cp\u003eElevation has a significant impact on flood dynamics increasing the flood vulnerability of low-lying areas. Water naturally travels downslope due to gravity, causing runoff from elevated terrains such as hills or uplands to accumulate in low-lying places such as river valleys and flood plains. This gravity movement when coupled with insufficient or overburdened drainage infrastructure, significantly contributes to flooding in flat or low-lying regions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A Digital Elevation Model (DEM) with a spatial resolution of 30 meters was used to create a thematic elevation map for this investigation. The elevation data was categorized using the natural breaks approach into five flood vulnerability zones: very high ( -23-3 m); high (3-6m); moderate ( 6-9m); low ( 9-12m); and very low ( 12-34m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Precipitation\u003c/h2\u003e\u003cp\u003ePrecipitation is the main cause of floods and waterlogs, with heavy rainfall in a short period of time being widely recognized as the dominant trigger (Tang et al., 2020). The amount of rainfall that ultimately contributes to river discharge depends to a large extent on the characteristics of the watershed, i.e. soil cover, slope and drainage capacity at the time of the event [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The data on precipitation for the period 2003 to 2023 were obtained from the data portal of the Centre for Hydrometeorology and Remote Sensing (CHRS). Multiple interpolation methods have been evaluated to improve spatial accuracy, with particular emphasis on the inverse weighted method using power parameters from 1 to 5 for the comparative analysis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The data were converted to a raster layer with the required resolution and reclassified into five classes of flood influence: very low (271.23-277.89 mm); low (277.89-281.73 mm); moderate (281.73-285.54 mm); high (285.54\u0026ndash;289.80 mm); and very high (289.80-294.62 mm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Land use/land cover (LULC\u003cem\u003e)\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eLand Use and Land Cover (LULC) is recognized as one of the most influential factors contributing to flood susceptibility, serving as a critical parameter in identifying areas at heightened risk of flooding [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. LULC significantly affects hydrological processes by altering infiltration rates, surface runoff accumulation, surface\u0026ndash;subsurface water interactions, and evapotranspiration dynamics[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Infiltration capacity varies greatly by land cover type; forested and vegetated regions often have high infiltration rates, whereas urbanized and stony surfaces tend to restrict water absorption, increasing the danger of flooding [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Land Use and Land Cover (LULC) mapping was conducted using Sentinel dat \u0026minus;\u0026thinsp;2A imagery obtained from the Copernicus Open Access Hub in 2023. The image was preprocessed, including geometric correction and resampling of the Level-2A result, to maintain uniform spatial resolution across all spectral bands. The imagery was subsequently mosaicked and spatially clipped to the study area using a shapefile and the Mask tool in ArcGIS 10.8 Desktop. The coordinate system was then converted to the geographic coordinate system, WGS84, from the Universal Transverse Mercator (UTM) projection. The Random Forest (RF) algorithm, a powerful ensemble learning technique that builds several decision trees and aggregates their outputs to increase classification accuracy and decrease overfitting, was used to classify LULC. This method improved the LULC map's overall accuracy and dependability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The LULC map was reclassified into five classes: water body, Wetland, built-up, agriculture, and vegetation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Normalized difference vegetation index (NDVI)\u003c/h2\u003e\u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) is a widely recognized remote sensing indicator that is frequently employed in flood risk assessment due to its capacity to capture land surface characteristics and vegetation health [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. NDVI effectively indicates vegetation cover, which significantly contributes to flood control by affecting infiltration and lowering surface runoff [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Its application is especially useful for identifying flood-prone locations, as sparse vegetation or impermeable surfaces are frequently associated with increased flood vulnerability [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The NDVI data used in this investigation were obtained from Landsat-8 satellite imagery. The index goes from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, with higher values representing thick vegetation and lower values representing bare ground, built-up regions, or aquatic bodies. Within the study area, NDVI values ranged from \u0026minus;\u0026thinsp;0.077 to 0.497 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), indicating a wide range of land cover types and surface characteristics related to flood dynamics. The NDVI was determined using the following formula:\u003c/p\u003e\u003cp\u003eNDVI = (\u0026#119873;\u0026#119868;\u0026#119877;\u0026minus;\u0026#119877;\u0026#119864;\u0026#119863;) / (\u0026#119873;\u0026#119868;\u0026#119877;+\u0026#119877;\u0026#119864;\u0026#119863;)\u003c/p\u003e\u003cp\u003ewhere NIR and RED denote the near-infrared and red spectral regions of Sentinel-2A imagery, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Drainage density\u003c/h2\u003e\u003cp\u003eDrainage density is defined as the ratio of total stream length to total watershed area and is an important indication of surface runoff behavior. Higher drainage density is often associated with increased surface water flow and a higher risk of flood events [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, drainage density was calculated using stream order data processed in ArcGIS 10.8. The Line Density function from the Spatial Analyst toolbox was used to calculate stream concentration throughout the watershed. The stream density map was divided into five categories based on natural breaks (Jenks) to indicate flood susceptibility levels: very low (0-22.1 km/km\u0026sup2;), low (22.11\u0026ndash;44.2 km/km\u0026sup2;), moderate (44.21\u0026ndash;66.3 km/km\u0026sup2;), high (66.31\u0026ndash;88.41 km/km\u0026sup2;), and very high (88.41\u0026ndash;110.5 km/km\u0026sup2;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eg).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Distance to rivers\u003c/h2\u003e\u003cp\u003eProximity to streams is important in identifying flood susceptibility since it helps identify places that are more likely to flood[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Numerous studies have found that areas near rivers are more susceptible to floods due to overland flow accumulation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]Areas farther from river networks, on the other hand, are less likely to experience floods [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Although there is no commonly accepted threshold distance for defining vulnerability, it is well understood that flood effect distances vary with river size. Small streams, for example, may only flood nearby areas, but huge rivers might damage areas several km distant [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, a distance-to-streams map was created utilizing stream order data and ArcGIS 10.8's buffer analysis tool. The output was divided into five zones based on flood influence: very high (0-0.02 m), high (0.02\u0026ndash;0.04 m), moderate (0.04\u0026ndash;0.06 m), low (0.06\u0026ndash;0.08 m), and very low (0.08\u0026ndash;0.10 m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eh).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Distance from Roads\u003c/h2\u003e\u003cp\u003eDistance from roads is considered a minor yet relevant factor influencing flood risk. Road networks often function as artificial barriers or embankments, affecting local water flow patterns and potentially obstructing or redirecting runoff. Consequently, there exists an inverse relationship between road proximity and flood susceptibility, with areas closer to roads generally experiencing lower flood risk due to elevated infrastructure or drainage planning. According to the Analytic Hierarchy Process (AHP) results, this factor ranked tenth in terms of relative importance among the variables considered in the flood susceptibility assessment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Both large and minor roads are digitalized in GIS after being extracted from OpenStreetMap. Ultimately, a raster dataset is generated using the GIS tool for Euclidean distance. The identified distance to the study area\u0026rsquo;s road map is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e(i).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Soil Type\u003c/h2\u003e\u003cp\u003eSoil type is critical in regulating surface runoff and infiltration processes during rainstorm events [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It has a big impact on how much water infiltrates the earth vs becomes surface runoff. Soils with inadequate infiltration capacity retain more surface water, which increases the chance of flooding [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Soil data for Jashore District were gathered from the Bangladesh Agricultural Research Council's (BARC) soil site. The specified shapefile was rasterised for spatial analysis. The study region consists of four principal soil types: clay, sandy clay, silty clay, and silty loam. Based on the standard classification, these reflect very high, high, moderate, and low flood susceptibility[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e](Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ej).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Assessment of flood susceptibility\u003c/h2\u003e\u003cp\u003eThe flood susceptibility map was derived by overlaying the previously identified influencing factors, weighted using the Analytical Hierarchy Process (AHP) analysis, and utilizing the weighted overlay analysis feature of the ArcGIS platform. The resulting map was classified into five susceptibility zones: very low, low, moderate, high, and very high-risk areas. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the flood susceptibility map. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the spatial distribution of the flood susceptibility zones, detailing their respective sizes and associated percentages of flood events. The flood susceptibility zones cover the following areas: 364.20 km\u0026sup2; (very low), 1397.63 km\u0026sup2; (low), 683.68 km\u0026sup2; (moderate), 273.02 km\u0026sup2; (high), and 83.68 km\u0026sup2; (very high). These areas correspond to the following percentages of the total region: 12.99% (very low), 49.88% (low), 24.39% (moderate), 9.74% (high), and 2.99% (very high). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e details the spatial distribution of these flood susceptibility classes within the study area.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpatial distribution and area coverage of flood susceptibility classes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuitability class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e364.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1397.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e683.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e273.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe current study identifies the upazilas of Manirampur, Keshabpur, and Avoynagar as being at high and very high flood risk. Specifically, the Kapalia region of Manirampur and the Avoynagar region exhibit a particularly high flood risk. Conversely, the Sharsa upazila falls within the moderate risk category. The upper Chaugacha upazila, along with Bagherpara and Jhikargacha upazilas, are classified as very low or low-risk zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of this study underline the significant impact of the topographic wetness index (TWI) on flood vulnerability. Areas with higher TWI in higher terrain slopes are associated with lower flood risk. The study shows a strong correlation between height and susceptibility zones, with higher heights generally associated with lower risk. Moreover, areas with gentle slopes tend to be less prone to flooding. Rainfall is the main driver of the increased risk in high-sensitive zones. Land use and land cover factors (LULCs) play a role in the dynamics of floods, but their overall weight in the final flood susceptibility model is limited because of the predominance of farmland in the area. Drainage density, although ranked lower in the AHP analysis than other factors, shows a significant impact, with areas with a higher drainage density generally having a lower flood risk. Moreover, regions with lower Normalized Difference Vegetation Index (NDVI) values are associated with an increased vulnerability to flooding. Although distance from rivers is an important factor, its impact on flood vulnerability is less pronounced in this study. The findings also highlight the significant impact of soil properties on the vulnerability of flood plains. Floodplain soil significantly increases flood susceptibility, while terraced landform areas present a lower flood risk. Although the overall impact of soil type is less pronounced in the flood vulnerability map, terraced land is particularly vulnerable to torrential rainfall. Several tactics can be used to lessen flooding. These include creating terraces to control and minimize surface runoff, planting trees to increase water retention, and establishing flood plains to redirect floodwaters. Additional engineering solutions to handle the excess water during floods include levees, dams, reservoirs, and retention ponds. Lastly, the study emphasizes how the El Nino Southern Oscillation (ENSO) and other global climatic events affect the frequency and severity of floods. El Navidad alters air circulation patterns, which often leads to unusual weather patterns like drought or heavy precipitation. In areas like Bangladesh, which are especially susceptible to monsoon storms, these impacts are especially significant. The necessity of incorporating global climate dynamics into local flood risk management measures is highlighted by the correlation between ENSO and flood susceptibility. The study highlights the interaction among various environmental and climatic factors offers a thorough understanding of flood vulnerability. The findings serve as a basis for the development of tailored mitigation strategies to reduce flood risks and boost resilience in susceptible places.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Discussion\u003c/h2\u003e\u003cp\u003eFloods take place every year in the south of the country, causing serious disruption and negative impacts on infrastructure, agriculture and human life, especially during the rainy season[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To reduce the detrimental consequences of flood events and assist effective disaster management initiatives, flood risk mapping, or FPM, is essential. This study shows that the Analytical Hierarchy Process (AHP) in conjunction with Geographic Information System (GIS) and Remote Sensing (RS) technology provides an effective approach for identifying flood-prone locations[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The study precisely identified regions in the Jashore district with varying flood susceptibility using ten flood-conditioning criteria, and it offered a solid framework for upcoming flood risk mitigation and management initiatives. Because of their immediate impact on water flows and buildup, previous research has shown that elevation, slope, land use and land cover (LULC), and distance from streams are some of the most significant causes of floods (Tehrany et al., 2019; [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These factors were also found to be crucial in determining the Jashore area's flood susceptibility. A thorough study was ensured by include ten elements, which is corroborated by earlier studies that indicate additional characteristics improve the reliability of FSM, especially in local studies with complex topography and changing hydrology (Md. Islam \u0026amp; Shahriar, 2023).\u003c/p\u003e\u003cp\u003eThe latest FSM divided the Jashore region into five flood risk zones: very low, low, medium, high, and very high. The findings revealed that the very low and low sensitivity zones were primarily found in the district's higher peaks and northern sections, whereas the moderate sensitivity zones were centered in the district's middle areas. In contrast, the south and south-east regions, which are characterized by low-lying plains and proximity to watercourses, are highly to extremely vulnerable to flooding. These findings are consistent with earlier investigations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which highlight the susceptibility of flat, low-elevation areas near rivers and streams to regular floods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Validation\u003c/h2\u003e\u003cp\u003eThe assessment of the developed flood susceptibility map for Jashore District involved a thorough evaluation of its reliability and practical applicability, utilizing a mix of internal consistency checks and qualitative validation in relation to established regional flood characteristics. This study introduces a comprehensive AHP-based flood susceptibility model for the area, but a traditional external validation using extensive, independently collected historical flood inventory data was not feasible. Nonetheless, the Analytical Hierarchy Process (AHP) incorporates an essential self-consistency measure. The computed Consistency Ratio (CR) for the pairwise comparison matrix was 0.37%, which is well below the 10% threshold, demonstrating a strong level of internal consistency and reliability of the assigned factor weights. Moreover, the spatial arrangement of the identified susceptibility zones closely corresponds with local insights and earlier studies regarding flood patterns in Jashore. High and very high-risk areas were precisely identified in low-lying plains marked by significant rainfall and closeness to rivers, including Kapalia, Manirampur, Avoynagar, and Keshabpur Upazila. In contrast, areas identified as low-risk were associated with elevated altitudes and steeper slopes, such as the uplands of Chowgacha and Jhikargacha. The qualitative alignment with established flood behavior in the region strengthens the confidence in the model's output and its effectiveness as a foundational tool for managing flood risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Model Limitations and Prospects for Advancement\u003c/h2\u003e\u003cp\u003eAlthough the AHP method has demonstrated effectiveness in FSM, some limits must be acknowledged. The inclusion of subjective expert assessment in a pairwise comparative matrix (PCM) can generate bias and inconsistencies. Furthermore, data availability and quality continue to be significant issues, particularly in locations where there have been substantial shifts in climate and environmental factors in recent decades. To address these constraints, advanced techniques such as fuzzy AHP, Artificial Neural Network (ANN), Linear Regression Models, and Hybrid Machine Learning Models could improve FSM reliability and accuracy. The study provides useful information for managing flood hazards in the Jashore area, as well as a trustworthy flood risk map that policymakers, urban planners, and local governments may use. The findings are used to inform land-use planning, infrastructure development, and disaster-preparedness measures aimed at reducing the harmful effects of future floods. Furthermore, this research helps to establish sustainable flood management practices and long-term policies to strengthen the Jashore area's flood resistance.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eAn adequate flood and water-logging risk assessment approach is essential for developing resilience communities in the face of climate change and urbanization. This study used a GIS-based Analytical Hierarchy Process (AHP) to Evaluate the spatial susceptibility to flooding and waterlogging in the Jashore district, incorporating 10 major criteria such as topographic wetness index (13.8%), precipitation (13.5%), elevation (12.1%), and proximity to rivers (14.1%). Based on weighted factor analysis, the region was divided into five susceptibility zones ranging from very low to very high, with high-risk zones located in flat, low-lying areas near rivers with inadequate drainage, both of which contribute to flooding and water logging. In contrast, low-risk locations have higher altitudes, steeper slopes, and land uses that are less susceptible to saturation. While the GIS-AHP framework was useful, it had limitations, such as the removal of subsurface drainage infrastructure and dynamic soil moisture characteristics, both of which are essential for evaluating waterlogging. Furthermore, utilizing specialist expertise for weight distribution adds subjectivity. Future studies should use hybrid models that incorporate expert insights with machine learning or hydrological simulation to increase accuracy and reproducibility. Despite these limitations, the findings provide a consistent framework for spatial flood analysis and water logging risk, which can help with disaster risk reduction, urban drainage planning, and adaptive land use strategies in Jashore and other flood-prone areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere gratitude to the Jashore Municipality for its support and cooperation in facilitating this study. We also acknowledge the United States Geological Survey (USGS) and Esri for providing free access to satellite imagery, the Bangladesh Agricultural Research Council (BARC) for supplying soil shapefiles, the Center for Hydrometeorology and Remote Sensing (CHRS) data portal for rainfall shapefiles, and OpenStreetMap for shapefiles related to river and road networks. All authors have read, understood, and, where applicable, complied with the ethical responsibilities outlined in the \u0026quot;Instructions for Authors\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRH: Conceptualization; methodology; formal analysis; mapping original draft; review; and editing; MIH: Formal analysis; methodology; review, supervision and editing; ZHS: Mapping methodology; review; and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and materials used to present the study findings are available upon a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. Faiz Ahmed and N. Kranthi, \u0026ldquo;Flood Vulnerability Assessment using Geospatial Techniques: Chennai, India,\u0026rdquo; \u003cem\u003eIndian J Sci Technol\u003c/em\u003e, vol. 11, no. 6, pp. 1\u0026ndash;13, Feb. 2018, doi: 10.17485/IJST/2018/V11I6/110831.\u003c/li\u003e\n\u003cli\u003eN. Islam and K. Dharanirajan, \u0026ldquo; Flood impact assessment in Murshidabad District of West Bengal using remote sensing and GIS. ,\u0026rdquo; \u003cem\u003eInt J Adv Remote Sens GIS\u003c/em\u003e, pp. 48\u0026ndash;57, 2017.\u003c/li\u003e\n\u003cli\u003eN. Logesh, T. Usha, G. Muthusankar, P. K. Bavinaya, M. Prabhakaran, and C. Lakshumanan, \u0026ldquo;Flood susceptibility analysis in Chennai Corporation using Frequency Ratio Model,\u0026rdquo; \u003cem\u003eJournal of the Indian Society of Remote Sensing\u003c/em\u003e, vol. 49, pp. 1533\u0026ndash;1543, 2021, Accessed: Jan. 19, 2025. [Online]. Available: https://hal.science/hal-03157379\u003c/li\u003e\n\u003cli\u003eB. M. R. Faisal and Y. S. Hayakawa, \u0026ldquo;Geomorphological processes and their connectivity in hillslope, fluvial, and coastal areas in Bangladesh: A review,\u0026rdquo; \u003cem\u003eProgress in Earth and Planetary Science 2022 9:1\u003c/em\u003e, vol. 9, no. 1, pp. 1\u0026ndash;22, Jul. 2022, doi: 10.1186/S40645-022-00500-8.\u003c/li\u003e\n\u003cli\u003eM. Becker \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Coastal Flooding in Asian Megadeltas: Recent Advances, Persistent Challenges, and Call for Actions Amidst Local and Global Changes,\u0026rdquo; \u003cem\u003eReviews of Geophysics\u003c/em\u003e, vol. 62, no. 4, p. e2024RG000846, Dec. 2024, doi: 10.1029/2024RG000846.\u003c/li\u003e\n\u003cli\u003eUllah. M. Shariot, \u0026ldquo;Sea-level rise and sustainable shore protection strategies in the low-lying delta: A case study of Bangladesh,\u0026rdquo; \u003cem\u003eReg Stud Mar Sci\u003c/em\u003e, vol. 71, Apr. 2024, doi: 10.1016/J.RSMA.2024.103424.\u003c/li\u003e\n\u003cli\u003eF. F. and W. C. of B. FFWC and W. D. B. BWDB, \u0026ldquo;Annual Flood Report,\u0026rdquo; 2016. Accessed: Jan. 19, 2025. [Online]. Available: http://www.ffwc.gov.bd/images/annual16.pdf\u003c/li\u003e\n\u003cli\u003eM. S. Khan and M. A. Hossan, \u0026ldquo;Water-logging and its\u0026rsquo; impacts on livelihood: A study from Jashore Sadar Upazila, Jashore, Bangladesh,\u0026rdquo; \u003cem\u003eJournal of Tropical Resources and Sustainable Science (JTRSS) \u003c/em\u003e, vol. 9, no. 1, pp. 58\u0026ndash;66, Aug. 2021, doi: 10.47253/JTRSS.V9I1.711.\u003c/li\u003e\n\u003cli\u003eZ. Ahmed and S. Ambinakudige, \u0026ldquo;Does land use change, waterlogging, and salinity impact on sustainability of agriculture and food security? Evidence from southwestern coastal region of Bangladesh,\u0026rdquo; \u003cem\u003eEnviron Monit Assess\u003c/em\u003e, vol. 195, no. 1, pp. 1\u0026ndash;28, Jan. 2023, doi: 10.1007/S10661-022-10673-W/FIGURES/25.\u003c/li\u003e\n\u003cli\u003eD. S. Bisht, C. Chatterjee, S. Kalakoti, P. Upadhyay, M. Sahoo, and A. Panda, \u0026ldquo;Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study,\u0026rdquo; \u003cem\u003eNatural Hazards\u003c/em\u003e, vol. 84, no. 2, pp. 749\u0026ndash;776, Nov. 2016, doi: 10.1007/S11069-016-2455-1/FIGURES/17.\u003c/li\u003e\n\u003cli\u003eS. E. Haque and N. Nahar, \u0026ldquo;Bangladesh: Climate Change Issues, Mitigation, and Adaptation in the Water Sector,\u0026rdquo; \u003cem\u003eACS ES and T Water\u003c/em\u003e, vol. 3, no. 6, pp. 1484\u0026ndash;1501, Jun. 2023, doi: 10.1021/ACSESTWATER.2C00450/ASSET/IMAGES/MEDIUM/EW2C00450_0005.GIF.\u003c/li\u003e\n\u003cli\u003eM. S. Tehrany, B. Pradhan, and M. N. Jebur, \u0026ldquo;Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method,\u0026rdquo; \u003cem\u003eStochastic Environmental Research and Risk Assessment\u003c/em\u003e, vol. 29, no. 4, pp. 1149\u0026ndash;1165, May 2015, doi: 10.1007/S00477-015-1021-9/FIGURES/9.\u003c/li\u003e\n\u003cli\u003eF. Pappenberger, P. Matgen, K. J. Beven, J. B. Henry, L. Pfister, and P. Fraipont, \u0026ldquo;Influence of uncertain boundary conditions and model structure on flood inundation predictions,\u0026rdquo; \u003cem\u003eAdv Water Resour\u003c/em\u003e, vol. 29, no. 10, pp. 1430\u0026ndash;1449, Oct. 2006, doi: 10.1016/J.ADVWATRES.2005.11.012.\u003c/li\u003e\n\u003cli\u003eO. Rahmati \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Development of novel hybridized models for urban flood susceptibility mapping,\u0026rdquo; \u003cem\u003eScientific Reports 2020 10:1\u003c/em\u003e, vol. 10, no. 1, pp. 1\u0026ndash;19, Jul. 2020, doi: 10.1038/s41598-020-69703-7.\u003c/li\u003e\n\u003cli\u003eH. Mojaddadi, B. Pradhan, H. Nampak, N. Ahmad, and A. H. bin Ghazali, \u0026ldquo;Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS,\u0026rdquo; \u003cem\u003eGeomatics, Natural Hazards and Risk\u003c/em\u003e, vol. 8, no. 2, pp. 1080\u0026ndash;1102, Dec. 2017, doi: 10.1080/19475705.2017.1294113.\u003c/li\u003e\n\u003cli\u003eM. Rahman \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh,\u0026rdquo; \u003cem\u003eJ Environ Manage\u003c/em\u003e, vol. 295, p. 113086, Oct. 2021, doi: 10.1016/J.JENVMAN.2021.113086.\u003c/li\u003e\n\u003cli\u003eB. T. Pham \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Flood risk assessment using deep learning integrated with multi-criteria decision analysis,\u0026rdquo; \u003cem\u003eKnowl Based Syst\u003c/em\u003e, vol. 219, p. 106899, May 2021, doi: 10.1016/J.KNOSYS.2021.106899.\u003c/li\u003e\n\u003cli\u003eC. Luu, J. von Meding, and M. Mojtahedi, \u0026ldquo;Analyzing Vietnam\u0026rsquo;s national disaster loss database for flood risk assessment using multiple linear regression-TOPSIS,\u0026rdquo; \u003cem\u003eInternational Journal of Disaster Risk Reduction\u003c/em\u003e, vol. 40, p. 101153, Nov. 2019, doi: 10.1016/J.IJDRR.2019.101153.\u003c/li\u003e\n\u003cli\u003eA. M. Al-Abadi, A. A. Al-Temmeme, and M. A. Al-Ghanimy, \u0026ldquo;A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra\u0026ndash;Al Al-Gharbi\u0026ndash;Teeb areas, Iraq,\u0026rdquo; \u003cem\u003eSustain Water Resour Manag\u003c/em\u003e, vol. 2, no. 3, pp. 265\u0026ndash;283, Sep. 2016, doi: 10.1007/S40899-016-0056-5/FIGURES/15.\u003c/li\u003e\n\u003cli\u003eD. S. Fern\u0026aacute;ndez and M. A. Lutz, \u0026ldquo;Urban flood hazard zoning in Tucum\u0026aacute;n Province, Argentina, using GIS and multicriteria decision analysis,\u0026rdquo; \u003cem\u003eEng Geol\u003c/em\u003e, vol. 111, no. 1\u0026ndash;4, pp. 90\u0026ndash;98, Feb. 2010, doi: 10.1016/J.ENGGEO.2009.12.006.\u003c/li\u003e\n\u003cli\u003eMd. T. Islam and Md. A. Shahriar, \u0026ldquo;Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques,\u0026rdquo; Oct. 2023, doi: 10.21203/RS.3.RS-3492845/V1.\u003c/li\u003e\n\u003cli\u003eD. Bosch, \u0026ldquo;Final Proof,\u0026rdquo; \u003cem\u003eAnn Intern Med\u003c/em\u003e, vol. 156, no. 12, p. 900, 2012, doi: 10.7326/0003-4819-156-12-201206190-00015.\u003c/li\u003e\n\u003cli\u003eM. S. Shadmaan and K. M. Hassan, \u0026ldquo;Assessment of flood susceptibility in Sylhet using analytical hierarchy process and geospatial technique,\u0026rdquo; \u003cem\u003eGeomatica\u003c/em\u003e, vol. 76, no. 1, p. 100003, 2024, doi: 10.1016/j.geomat.2024.100003.\u003c/li\u003e\n\u003cli\u003eT. L. Saaty, \u0026ldquo;How to make a decision: The analytic hierarchy process,\u0026rdquo; \u003cem\u003eEur J Oper Res\u003c/em\u003e, vol. 48, no. 1, pp. 9\u0026ndash;26, Sep. 1990, doi: 10.1016/0377-2217(90)90057-I.\u003c/li\u003e\n\u003cli\u003eZ. Kader, R. Islam, T. Aziz, M. Hossain, R. Islam, and M. Miah, \u0026ldquo;GIS and AHP ‑ based flood susceptibility mapping : a case study of Bangladesh,\u0026rdquo; \u003cem\u003eSustain Water Resour Manag\u003c/em\u003e, vol. 10, no. 5, pp. 1\u0026ndash;18, 2024, doi: 10.1007/s40899-024-01150-y.\u003c/li\u003e\n\u003cli\u003eH. Akay, \u0026ldquo;Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods,\u0026rdquo; \u003cem\u003eSoft comput\u003c/em\u003e, vol. 25, no. 14, pp. 9325\u0026ndash;9346, 2021, doi: 10.1007/s00500-021-05903-1.\u003c/li\u003e\n\u003cli\u003eL. Gigović, D. Pamučar, Z. Bajić, and S. Drobnjak, \u0026ldquo;Application of GIS-interval rough AHP methodology for flood hazard mapping in Urban areas,\u0026rdquo; \u003cem\u003eWater (Switzerland)\u003c/em\u003e, vol. 9, no. 6, pp. 1\u0026ndash;26, 2017, doi: 10.3390/w9060360.\u003c/li\u003e\n\u003cli\u003eM. S. Tehrany, L. Kumar, and F. Shabani, \u0026ldquo;A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia,\u0026rdquo; \u003cem\u003ePeerJ\u003c/em\u003e, vol. 2019, no. 10, p. e7653, Oct. 2019, doi: 10.7717/PEERJ.7653/SUPP-1.\u003c/li\u003e\n\u003cli\u003eR. U. Zzaman, S. Nowreen, M. Billah, and A. S. Islam, \u0026ldquo;Flood hazard mapping of Sangu River basin in Bangladesh using multi-criteria analysis of hydro-geomorphological factors,\u0026rdquo; \u003cem\u003eJ Flood Risk Manag\u003c/em\u003e, vol. 14, no. 3, p. e12715, Sep. 2021, doi: 10.1111/JFR3.12715.\u003c/li\u003e\n\u003cli\u003eS. E. Tabarestani and H. Afzalimehr, \u0026ldquo;A comparative assessment of multi-criteria decision analysis for flood susceptibility modelling,\u0026rdquo; \u003cem\u003eGeocarto Int\u003c/em\u003e, vol. 37, no. 20, pp. 5851\u0026ndash;5874, Oct. 2022, doi: 10.1080/10106049.2021.1923834.\u003c/li\u003e\n\u003cli\u003eX. Tang, J. Li, M. Liu, W. Liu, and H. Hong, \u0026ldquo;Flood susceptibility assessment based on a novel random Na\u0026iuml;ve Bayes method: A comparison between different factor discretization methods,\u0026rdquo; \u003cem\u003eCatena (Amst)\u003c/em\u003e, vol. 190, p. 104536, Jul. 2020, doi: 10.1016/J.CATENA.2020.104536.\u003c/li\u003e\n\u003cli\u003eK. Ullah and J. Zhang, \u0026ldquo;GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan,\u0026rdquo; \u003cem\u003ePLoS One\u003c/em\u003e, vol. 15, no. 3, p. e0229153, 2020, doi: 10.1371/JOURNAL.PONE.0229153.\u003c/li\u003e\n\u003cli\u003eH. Allafta and C. Opp, \u0026ldquo;GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran,\u0026rdquo; \u003cem\u003eGeomatics, Natural Hazards and Risk\u003c/em\u003e, vol. 12, no. 1, pp. 2087\u0026ndash;2116, 2021, doi: 10.1080/19475705.2021.1955755.\u003c/li\u003e\n\u003cli\u003eLappas I and Kallioras A, \u0026ldquo;Flood Susceptibility Assessment through GIS-Based Multi-Criteria Approach and Analytical Hierarchy Process (AHP) in a River Basin in Central Greece,\u0026rdquo; \u003cem\u003eInternational Research Journal of Engineering and Technology\u003c/em\u003e, vol. 738, 2008, Accessed: Jan. 26, 2025. [Online]. Available: www.irjet.net\u003c/li\u003e\n\u003cli\u003eM. Pal, \u0026ldquo;Random forest classifier for remote sensing classification,\u0026rdquo; \u003cem\u003eInt J Remote Sens\u003c/em\u003e, vol. 26, no. 1, pp. 217\u0026ndash;222, Jan. 2005, doi: 10.1080/01431160412331269698.\u003c/li\u003e\n\u003cli\u003eA. Saha \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms,\u0026rdquo; \u003cem\u003eWater 2021, Vol. 13, Page 241\u003c/em\u003e, vol. 13, no. 2, p. 241, Jan. 2021, doi: 10.3390/W13020241.\u003c/li\u003e\n\u003cli\u003eI. Chowdhuri, S. C. Pal, and R. Chakrabortty, \u0026ldquo;Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India,\u0026rdquo; \u003cem\u003eAdvances in Space Research\u003c/em\u003e, vol. 65, no. 5, pp. 1466\u0026ndash;1489, Mar. 2020, doi: 10.1016/J.ASR.2019.12.003.\u003c/li\u003e\n\u003cli\u003eS. Das and A. Gupta, \u0026ldquo;Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India,\u0026rdquo; \u003cem\u003eGeoscience Frontiers\u003c/em\u003e, vol. 12, no. 5, p. 101206, Sep. 2021, doi: 10.1016/J.GSF.2021.101206.\u003c/li\u003e\n\u003cli\u003eS. Rehman, M. Sahana, H. Hong, H. Sajjad, and B. Bin Ahmed, \u0026ldquo;A systematic review on approaches and methods used for flood vulnerability assessment: framework for future research,\u0026rdquo; \u003cem\u003eNatural Hazards\u003c/em\u003e, vol. 96, no. 2, pp. 975\u0026ndash;998, Mar. 2019, doi: 10.1007/S11069-018-03567-Z/FIGURES/7.\u003c/li\u003e\n\u003cli\u003eM. Hussain, M. M. Ajmal, M. Khan, and H. Saber, \u0026ldquo;Competitive priorities and knowledge management: An empirical investigation of manufacturing companies in UAE,\u0026rdquo; \u003cem\u003eJournal of Manufacturing Technology Management\u003c/em\u003e, vol. 26, no. 6, pp. 791\u0026ndash;806, 2015, doi: 10.1108/JMTM-03-2014-0020.\u003c/li\u003e\n\u003cli\u003eT. Islam \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Flood susceptibility modelling using advanced ensemble machine learning models,\u0026rdquo; \u003cem\u003eGeoscience Frontiers\u003c/em\u003e, vol. 12, no. 3, p. 101075, May 2021, doi: 10.1016/J.GSF.2020.09.006.\u003c/li\u003e\n\u003cli\u003eMd. Islam and Md. A. Shahriar, \u0026ldquo;Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques,\u0026rdquo; Oct. 2023, doi: 10.21203/RS.3.RS-3492845/V1.\u003c/li\u003e\n\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water Logging Risk, Flood Prediction Model, Analytical Hierarchy Process (AHP), Geographic Information System (GIS), Multi-criteria Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7106425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7106425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Jashore district in Bangladesh is facing persistent problems of flooding and landslides due to heavy rainfall, inadequate drainage systems and poor management of its water table. These problems are undermining livelihoods, undermining infrastructure and threatening the sustainability of the region's economy. The study introduces an innovative methodology for assessing the risks of deforestation. A comprehensive analysis of ten key factors - topographic wetness index (13.8), elevation (12.1), slope (9.9), land use and area (6.6), normalized difference vegetation index (5.3) - has helped to create a detailed spatial risk map. The findings classify the district as follows: very low (364.20 km), low (1397.63 km 2), medium (273.02 km\u003csup\u003e2\u003c/sup\u003e) and high (83.68 km). The high and very high risk areas, including the Kapalia area between Manirampur and Avoynagar, and Keshabpur Upazila, are characterized by low altitude, heavy rainfall, proximity to rivers and poor drainage. Conversely, low-risk areas such as the Chowgacha and Jhikargacha uplands have higher altitudes and more pronounced slopes. The analysis highlights the critical role of the topographical moisture index (TWI), with higher TWIs associated with less water scarcity, whereas elevation is the most important factor for water scarcity. The main cause of the flooding is the intensity of rainfall, compounded by poor drainage and poor management of waterways. Integrating AHP and GIS not only improves analytical accuracy, but also provides a visually intuitive and actionable tool for decision-makers. The study provides a visionary framework for tackling the root causes of flooding, promoting sustainable water management and driving forward climate change. The findings are a critical source for developing strategic interventions to mitigate the risks of flashpoint.\u003c/p\u003e","manuscriptTitle":"Flood and Water Logging Risk Assessment of Jashore District Leveraging the Analytical Hierarchy Process (AHP) Through a Spatial Analysis Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 13:10:36","doi":"10.21203/rs.3.rs-7106425/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eefac287-b4e6-47a7-8570-139ffa197a36","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T19:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 13:10:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7106425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7106425","identity":"rs-7106425","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}