Analysis on the Spatiotemporal Changes of Groundwater Potential Zone in Sylhet, Bangladesh: An AHP and GIS based approach

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Azizul Baten, Towfiqul Islam Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6898005/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 Groundwater Potential Zone (GWPZ) refers to the appropriate locations for groundwater recharge and infiltration. However, groundwater resources have been constantly under stress due to rapid population increases, contributing to the increase in impermeable structures. This research article aims to analyze spatiotemporal changes in GWPZ’s in Sylhet district, Bangladesh using Remote Sensing datasets, Geographic Information System techniques, and one of the Multi-Criteria Decision Making (MCDM) models known as Analytical Hierarchy Process (AHP). Nine influential parameters, such as Rainfall amounts, Surface Geology, Slope, Land Use Land Cover (LULC) with accuracy assessment, Drainage Density, Lineament Density, Hydrological Soil Group, Groundwater Depth below surface level, and Topographical Wetness Index have been weighted by the AHP decision matrix to identify GWPZ for the years 1998 and 2024 in Sylhet district using the weighted overlay analysis in ArcGIS. The northern part of the district has shown greater potential in both years, whereas the southern and eastern regions comparatively have lower potential. The GWPZ in 2024 at Sylhet exhibits a major shift in groundwater potentiality, particularly in the high potential zone categories, which decreased by approximately 16% compared to 1998. The increasing trend of the moderate potential zones in 2024 has also been identified. Modification of the GWPZ is examined by addressing the spatiotemporal changes in the three most influential variables, which are LULC changes, Groundwater Depth Deviation, and Rainfall Variability. Validation of the research is performed with the groundwater level data collected from Bangladesh Water Development Board (BWDB). The study reflects the critical need for sustainable groundwater managing policies to alleviate the declining trend in high-potential zones and ensure water security in the region. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Groundwater Potential Zones (GWPZ) Geographic Information System (GIS) Analytical Hierarchy Process (AHP) Spatiotemporal Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Groundwater (GW) is the most accessed freshwater resource which is important for sustaining the health of humans and the environment by supplying water, essential nutrients, and maintaining a relatively consistent temperature [1], [2]. It gets infiltrated through the surface and gets saturated in the pores and cracks on the soils and rocks underground [3], [4]. Groundwater potential zone (GWPZ) refers to areas where there is a strong chance of finding and extracting groundwater. Identifying the groundwater potential zones has become a global issue among the hydrologists and global researchers [5]. GWPZ identification seeks to address the issue of suitable site selection for groundwater extraction for managing groundwater resources and ensuring the sustainability of groundwater use [6]. Groundwater mainly occurs because of interaction between geological, climatic, biological, physiographical, and hydrological parameters including rainfall, geology, drainage pattern, slope, soil types, etc. [7], [8], [9]. The availability of sufficient rainfall is key to enhancing groundwater resources in any area and rainfall variability can influence the infiltration of water into the aquifer system [5], [10]. However, GW resources are under stress due to anthropogenic activities, such as rapid urbanization which have led to a persistent rise in water demand and reduced permeable surfaces [11], [12], [13], [14]. To understand anthropogenic interventions, examining Land Use/Land Cover (LULC) change provides a clear and insightful perspective [15], [16]. In Bangladesh, around 85% of the population relies on groundwater, with 90% of it being utilized in the agricultural sector [17], [18]. As one of the most densely populated developing countries in the world, Bangladesh is experiencing unplanned urban growth, population increase, industrial activities, and extensive agricultural practices in the shrinking cultivated land [19], [20], [21]. Such events are the main causes of altering groundwater quality and quantity in the country, including Sylhet district in the northeastern region [22], [23], [24], [25]. GIS, RS technologies, and GEE platform have widespread applications for analyzing geospatial data and natural resources [26], [27]. Utilizing GIS and RS tools to monitor GWPZ is a useful, cost and time-efficient technique [28], [29]. With the increased efficiency of satellite data, GIS, and RS, delineation of GWPZ has become more accurate and convenient in recent years [30], [31]. To demarcate GWPZ, applications of several multivariate statistical models, including frequency ratio [32], logistic regression [33], fuzzy-AHP [34], and MCDM model, named AHP [35], [36] have been widely integrated with GIS in recent decades. AHP is a highly effective MCDM method capable of determining the relative importance of thematic layers for evaluating groundwater potentiality [13], [37]. However, in terms of determining LULC, GEE is a JavaScript and python coding geospatial analysis platform which is easier, powerful, and efficient tool that provides several classification algorithms with higher accuracy that minimizes image quality problems [38], [39], [40]. Groundwater resources are steadily declining in Sylhet district, Bangladesh, due to human activities and environmental changes. As a result, persistent monitoring and identifying of GWPZ is necessary for effectively managing GW resources. There have been several research on evaluating the spatiotemporal variations of groundwater level and quality in the selected study area [25], [41], LULC changes [42], [43], [44], and its impact on water quality [45]. However, studies on the determination of GWPZ in the study area as well as comprehending the shifting of GWPZ combining GEE, GIS, RS, and MCDM techniques in Sylhet district haven’t been conducted yet. The findings of this study will provide 1) GWPZ mapping using nine relevant parameters in Sylhet for 1998 and 2024, as well as 2) How the three temporal variables (rainfall variability, groundwater level fluctuation, and land use land cover changes) contributed to the probable shifting of GWPZs. The insights gained from this research will help comprehend the groundwater dynamics in Sylhet support hydrologists and policymakers to comprehend groundwater stability and improve mitigation strategies in the research area. 2. Material and Methods 2.1 Study Area Sylhet district is located in the northeastern part of Bangladesh. It has total area of 3416 square kilometers and is placed between 24°36' and 25°11' north latitudes and between 91°38' and 92°30' east longitudes (Fig. 1 ). Sylhet is situated downstream of the Meghalaya Hills of India. It is located within the Surma and Kushiyara floodplain, surrounded by the geographical characteristics of the hilly areas in the tertiary period, and characterized by an uneven geomorphic pattern. The primary land use in this area consists of depressions, farmlands, and settlement areas [46]. The research area lacks a central water distribution network; hence GW is the primary source of water and the majority of the area's residents are supplied with water by both shallow and deep aquifers [25], [41]. The aquifers are either confined or semi-confined and is comprised of the Dupi Tilla formation’s weathered alluvial sands, young gravelly sands with mixed range of permeability [25]. The population of Sylhet District was 3,857,037 in the Bangladesh Census of 2022, with the population density of 1117 people per square kilometers (Bangladesh Bureau of Statistics Census, 2022). The enormous population places a substantial strain on groundwater resources, causing them to be overused. Understanding where groundwater potential zones are located and how it is stressed under anthropogenic intervention helps in the sustainable management of water resources. By monitoring these zones, water can be extracted in a controlled manner that prevents overexploitation and ensures long-term availability. Data from 15 observation stations were collected in the study area (Fig. 1 ) for 1998 and 2024 for the delineation of GWPZ. 2.2 Method For identifying GWPZ, nine influential thematic layers are generated and analyzed using conventional and temporal data through ArcGIS and Remote Sensing tools. The conventional layers consist of geological data, drainage density, Hydrological Soil Group (HSG) classification, lineament density, slope measurements, and Topographical Wetness Index (TWI) maps. Rainfall variability, groundwater level fluctuation, and Land Use Land Cover (LULC) map with accuracy assessment are specifically generated for 1998 and 2024. Subsequently, Analytic Hierarchy Process (AHP) method is utilized for calculating the weights for 9 parameters with a consistency ratio (CR). The generated raster of each parameter was aggregated in ArcMap to generate the GWPZ map. Additional geospatial analyses were conducted for analyzing how temporal variables impact GWPZ. The methodology illustrated sequentially in Fig. 2 . 2.3 Data Source and Creation of Thematic Layers Traditional data for creating thematic layers are collected from multiple sources (Table 1 ). The SRTM Digital Elevation Model (DEM) data was extracted from the United States Geological Survey website ( https://earthexplorer.usgs.gov/ ) with a 30-meter resolution which has been used to delineate the slope, drainage density, lineament density, and topographical wetness index using the ArcMap 10.8. Precipitation data was gathered from Bangladesh Meteorological Department’s website to determine rainfall variability for 1998 and 2024. The hydrological soil group (HSG) has been prepared from the data imported from Food and Agricultural Organization (FAO) website ( https://www.fao.org/ ), scaled at 1:5000000. The groundwater depth data was provided by Bangladesh Hydrological Department ( http://www.hydrology.bwdb.gov.bd/ ), which was spatially analyzed and displayed by using inverse distance weight (IDW) method in ArcMap. The Landsat 5 Thematic Mapper (TM) and Sentinel-2 Surface Reflectance (SR) Level 2A Data, were utilized to classify various land-use categories using Random Forest (RF) method in GEE platform. Random Forest (RF) Machine learning classification algorithm has been shown to surpass other methods such as maximum likelihood, Support Vector Machine (SVM), decision trees (DT) and neural networks due to its higher accuracy [47], [48], [49]. Table 1 Data Sources and Details Data Type Source Details Uses Elevation (DEM) USGS Earth explorer ( https://earthexplorer.usgs.gov/ ) 30 m resolution Slope, Drainage Density, Lineament Density, Topographic wetness index Precipitation data Bangladesh Meteorological Department ( https://live8.bmd.gov.bd/ ) 30*30 cell size Precipitation Map (1998, 2024) Satellite imagery USGS Earth explorer ( https://earthexplorer.usgs.gov ), European Space Agency (ESA) 30 m resolution (1998), 10 m resolution (2024)- resampled to 30*30 cell size LULC map (1998, 2024) Geological map USGS Earth explorer ( https://earthexplorer.usgs.gov ) 30*30 cell size Geology Map Soil Food and Agricultural Organization (FAO) ( https://www.fao.org/ ) Scale 1:5000000, 30*30 cell size Hydrological Soil Group Groundwater Depth Bangladesh Hydrological Department ( http://www.hydrology.bwdb.gov.bd/ ) Meters below ground level (mbgl) Groundwater Level Map for 1998 and 2024 2.5 Accuracy Assessment for LULC Classification Accuracy assessment is an essential step in processing remote sensing data that evaluates the accuracy of pixels of the LULC categories [50], [51].This study implies the following formula of kappa statistical analysis to measure the accuracy of LULC. In this formula, P o refers to the relative observed agreement among raters. It refers to the proportion of instances where both raters agree out of the total number of instances whereas P e indicates the hypothetical probability of chance agreement. This is the probability that the two raters would agree by chance alone. It is calculated using the marginal probabilities of each category being chosen by the raters. This statistical method is useful for assessing the reliability of subjective measurements. 2.6 AHP method and the delineation of GWPZ Developed by Saaty (1980) [52], The AHP is a hierarchically structured approach that uses deconstruction, comparison judgments, and priority synthesis to analyze and solve difficult decision-making challenges. The integration of AHP and GIS platform allows for the systematic evaluation of various factors and criteria, leveraging spatial data to make informed decisions regarding the suitability or compatibility analysis for specific purposes [53], [54]. Many researchers have combined the (AHP) with RS data and GIS technology to delineate GWPZs [55], [56]. The AHP methodology involves constructing pairwise comparison matrices, where criteria are systematically compared against each other to determine their relative ranks or weights [57], [58]. Thematic layers were converted into raster datasets, where each pixel was sized at 30 meters by 30 meters. This process assigns comparative weights according to their impact on groundwater occurrence, drawing from expert opinions and relevant literature reviews. Table 2 displays the pair-wise comparison matrix and normalized weights for the nine parameters. Consistency ratio (CR) indicates to the accuracy of the judgement, which must be less than or equal to 0.1. The CR is calculated from the following equation: CR = \(\:\:\frac{\text{CI}}{\text{RI}}\) Here, the term RI stands for the Random Consistency Index, and CI stands for the Consistency Index, calculated as shown below: In this formula, 𝜆 represents the principal eigenvalue of the matrix, while n denotes the number of factors considered in the estimation (Saty 1980). Table 2 Pairwise Comparison Matrix Layers R G S DD LULC LD HSG GL TWI R 1 3 2 1 0.33 2 2 1 2 G 0.33 1 1 2 0.33 3 1 0.5 2 S 0.5 1 1 3 0.33 1 2 0.5 3 DD 1 0.5 0.33 1 0.5 2 1 0.5 0.33 LULC 3 3 3 2 1 3 4 3 5 LD 0.5 0.33 1 0.5 0.33 1 3 0.5 0.5 HSG 0.5 1 0.5 1 0.25 0.33 1 0.33 0.5 GL 1 2 2 2 0.33 2 3 1 3 TWI 0.5 0.5 0.333 3 0.2 2 2 0.33 1 SUM 8.33 12.33 11.16 15.5 3.61 16.33 19 7.66 17.33 Here, the matrix value 1 refers to the equal importance of the parameters to the objective. The values 3, 5, 7, and 9 in the AHP matrix signify the increasing importance of the layers relative to one another. Other intermediate values such as 2,4,6,8 can also be used for building the matrix. The weights allocated to the study area are determined by a combination of insights from previous literature and an analysis of the region's geographical characteristics. These factors provide a comprehensive basis for the weighting process, ensuring that both historical data and the specific attributes of the area are considered. The weights, principal eigenvector, and the consistency ratio are calculated in Table 3 . In the study area, rapid urbanization coupled with extensive agricultural activity makes LULC the most influential factor in delineating GWPZ. The temporal variables, including groundwater level fluctuations and average rainfall, have been assigned the following rankings. Among the fixed variables, slope is prioritized first due to the presence of steppe hills within the study area; however, it ranks fourth overall. The geological characteristics follow slope in importance, with drainage density, lineament density, and the topographical wetness index subsequently ranked. The hydrological soil group is given the least weight in the analysis, as the study area is characterized by a single, uniform soil type. Finally, GWPI of Sylhet was generated from using reclassified raster maps of the parameters and their corresponding weights combined into the weighted overlay tool in ArcGIS Platform. Such assessments of potential groundwater recharge sites are essential requirements for effective land use planning and management. They help improve both the general situation for managing water and land resources. The GWPI equation is as follows: GWPI = [Rr × Rw + Gr × Gw + Sr × Sw + DDr × DDw + LULCr × LULCw + LDr × LDw + HSGr × HSGw + GLr × GLw + TWIr × TWIw] Here, the acronyms represent R (Precipitation/ Rainfall), G (Geology), S (Slope), DD (Drainage Density), LULC (Land Use and Land Cover), LD (Lineament Density), HSG (Hydrological Soil Group), GL (Groundwater Level), TWI (Topographical Wetness Index). The superscript ‘w’ and ‘r’ refers to the weight of each parameter and the specific rank for each subclass of a parameter, determined by their relative importance in assessing groundwater potentiality. Table 3 Weights, and Consistency Ratio of the parameters Layers R G S DD LULC LD HSG GL TWI Weight R 0.117693 0.09375 0.166667 0.061538 0.100502513 0.147541 0.102564 0.137931 0.197802 0.12511 G 0.117693 0.09375 0.166667 0.123077 0.100502513 0.147541 0.102564 0.045977 0.065934 0.107078 S 0.038839 0.03125 0.055556 0.061538 0.100502513 0.04918 0.051282 0.068966 0.021978 0.053232 DD 0.117693 0.046875 0.055556 0.061538 0.060301508 0.016393 0.205128 0.068966 0.065934 0.077598 LULC 0.35308 0.28125 0.166667 0.307692 0.301507538 0.147541 0.205128 0.413793 0.32967 0.278481 LD 0.039231 0.03125 0.055556 0.184615 0.100502513 0.04918 0.025641 0.034483 0.021978 0.060271 HSG 0.058847 0.046875 0.055556 0.015385 0.075376884 0.098361 0.051282 0.045977 0.032967 0.053403 GL 0.117693 0.28125 0.111111 0.123077 0.100502513 0.196721 0.153846 0.137931 0.197802 0.15777 TWI 0.039231 0.09375 0.166667 0.061538 0.060301508 0.147541 0.102564 0.045977 0.065934 0.087056 Principal Eigenvector = 9.941 , Consistency Index = 0.12 , Random Index for 9*9 matrix = 1.45 Consistency Ratio = 0.08 < 0.1, meaning that the comparison matrix is consistent. 3. Result and Discussion Findings on the selected parameters, groundwater potential zone delineation and how it is shifted by temporal variables are analyzed in the following texts. 3.1 Rainfall Variability Rainfall amount in a region directly influences the amount of groundwater availability, by infiltrating through the surface and recharging groundwater aquifers. Water infiltration into the aquifer system may be influenced by variations in rainfall amounts. As a result, the evaluation of the groundwater potentiality has been greatly influenced by spatiotemporal fluctuation in rainfall amounts. The annual rainfall map for 1998 and 2024 has been generated (Fig 3a, 3b) by the IDW method using the rainfall datasets from Bangladesh Meteorological Department (BMD). The northern part of Sylhet received the highest amount of rainfall for both these years, with the amount gradually declining in the south. The highest rainfall amount increased from 3129 mm/year in 1998 to 4370 mm/year in 2024, similar in terms to the lowest amount which also increased from 2200 mm/year to 2570 mm/year in 2024. The areas with higher rainfall usually contribute to rich groundwater resources. 3.2 Slope Slope is a major factor affecting groundwater infiltration. Gentle slopes allow water to percolate into the soil, enhancing infiltration, but steep slopes lead to fast runoff and less water absorption into the aquifers [59]. Sylhet is dominated by nearly flat (0-2 degrees) and very gentle slopes (2-5 degrees). Higher sloped areas are found in central Tilla and northern regions (Fig 3c). Steppe areas are given lesser weights for groundwater potentiality. The natural break classification is used to illustrate the slope differences in the study area. 3.3 Geology Geological mapping is essential for assessing groundwater systems, highlighting the geological structures that determine groundwater presence and flow [60]. The study area consists of a versatile range of geological features (Fig 3d), dominated by alluvial silt and clay covering over 50% of the entire area, followed by marsh clay and peat with 22.7%. The other most significant types of geology present in Sylhet are young gravelly sand and Dihing and Dhupi Tilla formations (slightly elevated terrains) with 11.7% and 7.65% areas respectively. The geological features also include formations from the Miocene, Neogene, and Oligocene eras, and weight was assigned according to their potentiality. The young gravelly sand, tipam sandstone, and the ancient geologic forms were assigned high to moderate ranks due to their properties, whereas the clay formations were assigned lower ranks for groundwater infiltration. 3.4 Drainage Density Drainage density is the measurement of the total length of channels per unit area, reflecting how closely the channels are spaced [61]. It is a valuable measurement of how permeable the ground is in that region, therefore serving as a useful factor for assessing groundwater dynamics and potentiality [62], [63]. Drainage density is inversely related to the permeability of a region, meaning areas with higher drainage density have higher runoff rates, leading to lower groundwater potential and vice versa [64]. The drainage density in Sylhet is classified into five categories, starting from 0.11 to 20.32 km/sq km, with the majority being dominated by areas within 8.19 sq/km (Fig 3e). 3.5 Lineament Density (LD) Lineaments, usually linear joints and fractures are generally formed due to tectonic stress and strain and provide valuable insights into surface features that play a crucial role in the infiltration of surface runoff into aquifers and facilitate the movement and storage of groundwater [65], [66], [67]. Areas with greater lineament density tend to have higher groundwater potential, as the increased presence of lineaments enhances surface permeability [68]. In Sylhet, lineament density starts from no lineament (0) to high level density, peaking at 1.73 km/sq km (Fig 3f). The areas having higher lineament density were assigned higher ranks and weights according to their groundwater potentiality. LD is identified using the SRTM DEM (30m) data and utilizing the hill shade and line density tool in ArcMap 10.8 platform. 3.6 Land Use Land Cover (LULC) Change Analysis Land use and land cover change impacts the global water cycle and it’s considered as one of the most crucial roles in determining the occurrence and recharging of groundwater [69], [70]. The overuse of groundwater primarily results from the rapid expansion of agriculture and the development urban infrastructures [71], [72]. In this research, five primary LULC classes were identified in Sylhet for 1998 and 2024: water bodies, vegetation, cropland, built-up areas, and barren land (Fig 3g, 3h). LULC map for 1998 was generated from the Landsat 5 TM and Sentinel-2 datasets for 2024, utilizing the ArcMap and Google Earth Engine platforms. The land cover statistics and changing extent for both these years are given in Table 4. The central region of the district, particularly where Sylhet city is located, is predominantly characterized by built-up areas. By 2024, built-up areas had expanded by 26%, contributing to a reduction in groundwater potential due to impervious surfaces. Additionally, water bodies, both permanent and seasonal, experienced an 11% decline during this period, alongside a decrease in vegetation cover. Conversely, agricultural fields were increased by 57 square kilometers, resulting in higher groundwater extraction for meeting growing demands of the expanding population. The Kappa co-efficient accuracy values for each LULC maps are 0.83 and 0.87 respectively. Table 4: LULC Statistics for 1998 & 2024 LULC Classes Area in 1998 (sq_km) Area in 2024 (sq_km) Change (%) Relation to Groundwater Potentiality Built Up Area 128.20 182.50 29.75 Negative Barren Land 9.54 4.78 -99.59 Positive Cropland 1701.55 1758.66 3.25 Negative Vegetation 784.57 759.25 -3.33 Negative Water Bodies 790.30 710.98 -11.16 Negative Total 3414.16 3416.16 3.7 Groundwater Level (GL) The annual groundwater aquifer data from 15 wells were collected from Bangladesh Hydrological Department’s website for both 1998 and 2024 and visualized using the IDW interpolation technique in ArcMap 10.8 (Fig 3i, 3j). In 1998, the maximum depth at which groundwater was located was 4.62 meters below the surface, with the minimum depth recorded at 1.56 meters. By 2024, there was a marked decrease in the highest groundwater depth, which dropped to 8.85 meters, a significant change from the 4.62 meters observed in 1998. Similarly, the minimum depth where groundwater was detected also exhibited a decline, dropping to 1.77 meters below the surface. There is clear downward fluctuation in the observed groundwater level, which might be resulted from natural factors, such as the variation in rainfall, and manmade activities, such as excessive groundwater pumping, land use alterations, etc. 3.8 Hydrological Soil Group (HSG) The U.S. Department of Agriculture’s (USDA) Natural Resources Conservation Service (NRCS) classifies soils into four categories—A, B, C, and D—based on how well they absorb water when which affects their potential for groundwater recharge [73]. Table 5 shows the characteristics of these soil groups based on their infiltration characteristics. Table 5: Hydrological Soil Group Characteristics Soil Type Infiltration Rate (inch/hour) Potential A >0.30 High B 0.15-0.30 Moderate C 0.05-0.15 Low D 0-0.5 Very Low There are also subdivisions in these soil categories. In Sylhet, there is only type C soil, further subdivided into three categories (Fig 3k). These are C-Loam, C-Clay Loam, C-Clay. Among them, Loam has the largest area coverage with the highest groundwater potentiality among the three groups, followed by clay loam, and clay, and their weights are assigned accordingly. The HSG map was generated by the data retrieved from the Food and Agricultural Organization (FAO). Table 6: Hydrological Soil Group Classification in Sylhet. HSG Area (sq_km) Percentage C-Loam 1763 51.6 C-Clay Loam 1589.85 46.54 C-Clay 62.74 1.83 Total 3416.10 100 3.9 Topographical Wetness Index (TWI) The Topographical Wetness Index (TWI) indicates how the physical characteristics of the landscape—specifically its shape and slope—affect the way water accumulates and distributes across the surface [74]. It quantifies the relationship between upslope water flow and the steepness of the terrain. Greater TWI values indicate areas that are favorable for water accumulation and saturation. On the contrary, lower TWI values are associated with steeper slopes or ridges, where water flows quickly and is less likely to accumulate or infiltrate. The TWI value for Sylhet was delineated using the SRTM DEM (30 m) data (Figure 3l). 3.10 Evaluation of Groundwater Potential Zones and Spatiotemporal Analysis for 1998 and 2024 GPWZ’s for 1998 and 2024 (Figure 4a & 4b) in Sylhet was identified using nine distinct influential factors that influences groundwater occurance. Significant temporal variability, and changes in spatial extent has been observed in rainfall extent, groundwater level, and LULC classes across the study area. These temporal modifications in groundwater-controlling parameters can potentially shift the location and extent of GWPZ. The groundwater potential maps of Sylhet were categorized into five distinct classes: very low, low, moderate, high, and very high potential zones. Table 7 represents the potential zone areal statistics for 1998 and 2024. From 1998 to 2024, the groundwater potential zones (GWPZ) showed significant changes. Very low potential zones increased by 12.86%, from 18.58 sq. km (0.54%) to 21.32 sq. km (0.62%). Low potential zones experienced a minor growth of 1.91%, rising from 918.47 sq. km (26.89%) to 936.37 sq. km (27.41%). Moderate potential zones, the largest category, expanded by 4.22%, from 1,725.13 sq. km (50.50%) to 1,801.16 sq. km (52.72%). The major shift is observed for the high potential zones, which declined significantly by 15.97%, decreasing from 737.95 sq. km (21.60%) to 636.35 sq. km (18.63%). Very high potential zones exhibited slight growth, increasing by 23.53%, from 16.03 sq. km (0.47%) to 20.96 sq. km (0.61%). The detailed shifting GWPZ map is shown in Fig-5a, where negative change outweighs positive. These changes reflect dynamic shifts in groundwater potential zone, resulted from increased settlement areas and fluctuated groundwater depth, despite having higher amounts of rainfall in 2024. The GWPZ shift due to LULC class transformation and groundwater depth deviation is given in (Fig 5b, 5c). Table 7: Areal Statistics for GWPZ in 1998 and 2024 GWPZ Category Area in 1998 (sq_km) Percantage (%) Area in 2024 (sq_km) Percentage Change (%) Very Low 18.58 0.54 21.32 0.62 12.85 Low 918.47 26.89 936.37 27.41 1.91 Moderate 1725.13 50.50 1801.16 52.72 4.22 High 737.95 21.60 636.35 18.63 15.96 Very High 16.03 0.47 20.96 0.61 23.52 Total 3416.16 3416.16 3.11 Validation The evaluated GWPZ for 1998 and 2024 and it’s shifting analysis are validated against the groundwater level data for 15 wells retrieved from Bangladesh Water Development Board (BWDB). The data correlates reasonably well with trends observed with the datasets from the BWDB, showing a general decline in groundwater potential zones for 2024 in areas experiencing reduced GL. Table 8: Validation of GWPZ Well ID LAT LONG GL98 (m) GWPZ (1998) GL24 (m) GWPZ 2024 GL Deviation (%) GWPZ Shift on Map GT9108001 24.68 91.88 2.72 Moderate 1.77 High 35.04 Positive Change GT9108002 24.77 91.87 3.59 Low 5.4 Low -50.37 No Change GT9117004 24.85 92.18 3.74 Moderate 6.67 Low -78.57 Negative Change GT9117005 24.89 92.15 3.91 Low 4.47 Low -14.27 No Change GT9120006 24.75 91.75 3.78 Low 5.33 Low -41.15 No Change GT9135008 24.65 91.98 4.11 Moderate 4.64 Low -12.9 Negative Change GT9141013 25.16 92.06 3.07 Moderate 0.99 High 67.8 Positive Change GT9141015 25.00 91.92 4.37 Moderate 8.86 Low -102.89 Negative Change GT9159018 24.97 92.27 2.55 Moderate 2.85 Low -11.92 Negative Change GT9153012 24.93 91.99 1.61 Moderate 2.07 High -28.88 Positive Change GT9153016 25.09 92.13 1.56 High 3.7 Moderate -137.75 Negative Change GT9141016 24.98 92.33 4.36 Low 5.17 Low -18.64 No Change GT9162020 24.92 91.84 1.91 Moderate 3.67 Low -91.96 Negative Change GT9162022 24.93 91.88 4.62 Low 3.72 Moderate 19.41 Positive Change GT9162024 24.84 92.16 2.2 High 2.75 Moderate -24.9 Negative Change 4. Conclusion By integrating RS datasets, GIS techniques, and MCDM approach, the transformation of GWPZ in Sylhet for 1998 and 2024 has been analyzed. The reduced extent (15.92%) of high potential zone, and the increasing trend of moderate potential zone (76 square kilometers) has been notably observed in this research. The high GWPZ is primarily seen in the northern areas, immediately south to the Meghalayan foothills, and the lower GWPZ dominates on the central and southwestern parts, due to urban expansion and extensive agricultural practices coupled with lesser rainfall amounts. In recent years, the occurrence of groundwater extraction for meeting the growing demands for irrigation is posing a significant threat to groundwater storage and the sustainability of groundwater resources. Excessive extraction in areas with limited recharge capacity, might cause a rapid decline in groundwater level below the surface, potentially shifting groundwater potential zones toward lower categories. This highlights the urgency for efficient water management practices and policies for balancing agricultural needs with the preservation of groundwater resources for future generations. This methodology can be further approached by integrating various combinations of remote sensing layers to enhance the precision of identifying potential groundwater zones. By leveraging advanced geospatial techniques and incorporating additional environmental and hydrological parameters, the approach can provide more accurate and site-specific insights for sustainable groundwater management and planning. Declarations Author Contributions Utso Soumyo Talukdar prepared the manuscript, developed the methodology, groundwater potential zone identification, and spatiotemporal change analysis of groundwater potential zone. Milon Bokshi prepared figures 3g and 3h (Land Use Land Cover Map 1998 & 2024) and supervised the analysis and manuscript writing. Dr. Md. Azizul Baten and Towfiqul Islam Khan assisted on AHP matrix construction and supervised manuscript writing. Ethics and Consent to Publish declarations We affirm that this manuscript is original and represents our own work. It has not been previously published and is not being considered for publication by any other journal. All authors provided written consent for the publication of this article. This study did not include any human or animal participants. Competing Interests All funds of the research has been self funded. All authors have reviewed and approved the manuscript, and we confirm that all those who qualify as authors are included. The authors declare that there is no competing interests regarding the publication of this paper. Data Availability Groundwater level data obtained from Bangladesh Water Development Board (BWDB) and rainfall data from Bangladesh Meteorological Department: https://live8.bmd.gov.bd/. References Serele, C., Pérez-Hoyos, A., & Kayitakire, F. (2020). 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Hydrological Sciences Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6898005","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477117845,"identity":"13f88921-987b-4191-8f20-7812288f71c0","order_by":0,"name":"Utso Soumyo Talukdar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYHAD5gNAQkKGsEI2BCsBpIWHFC08BmCSoA6D++0PH/6oOByt297z+dWNGgseBvbDRzfg1XKMx9iY58zh3G1nzm6zzjkGdBhPWtoNAlrYpBnbgFpu5G4zzmEDapHgMSOghf2Z5E+wlpxnxjn/iNLCYCbBC9HC/Di3jQgtksdyQH5JB/rlmBlzbp8EDxshv/AdPg4KMevcbcebH3/O+VYnx89++BheLciATQJMEqscBJg/kKJ6FIyCUTAKRg4AAO/YSiHU3U5VAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Water Modelling","correspondingAuthor":true,"prefix":"","firstName":"Utso","middleName":"Soumyo","lastName":"Talukdar","suffix":""},{"id":477117847,"identity":"2990e537-890f-46d4-8791-82a97268ddfb","order_by":1,"name":"Milon Bokshi","email":"","orcid":"","institution":"Institute of Water Modelling","correspondingAuthor":false,"prefix":"","firstName":"Milon","middleName":"","lastName":"Bokshi","suffix":""},{"id":477117851,"identity":"0a328dad-5f06-4849-83f6-b6a3ac261036","order_by":2,"name":"Md. Azizul Baten","email":"","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Azizul","lastName":"Baten","suffix":""},{"id":477117856,"identity":"8e19b11a-06b7-4782-867b-b1f73849c9b2","order_by":3,"name":"Towfiqul Islam Khan","email":"","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Towfiqul","middleName":"Islam","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2025-06-15 11:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6898005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6898005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85764093,"identity":"60f66a41-54c7-4328-8aff-df9442eca858","added_by":"auto","created_at":"2025-07-01 12:08:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305560,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6898005/v1/1997806f0fe2b4d6ce2d10bd.jpg"},{"id":85764562,"identity":"c4fff606-b997-4858-bb0a-5eb0a5199804","added_by":"auto","created_at":"2025-07-01 12:16:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170097,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Methodology\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6898005/v1/9f3468639ebf30dc94c96f1c.png"},{"id":85764062,"identity":"58172749-2e4f-4e4f-8416-555f7a16bc9f","added_by":"auto","created_at":"2025-07-01 12:08:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1074557,"visible":true,"origin":"","legend":"\u003cp\u003ea) Annual Rainfall 1998, b) Annual Rainfall 2024, c) Slope, d) Geology e) Drainage Density, f) Lineament Density g) Land Use Land Cover (1998), h) Land Use Land Cover (2024), i) Groundwater Level 1998, j) Groundwater Level 2024, k) Hydrological Soil Group (HSG), l) Topographical Wetness Index (TWI)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6898005/v1/93735530fdd9da77ec2b1278.png"},{"id":85764569,"identity":"ecf8cd18-dae9-419f-8e73-897ecb79f41b","added_by":"auto","created_at":"2025-07-01 12:16:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":227802,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Groundwater Potential Zone In 1998, (b) Groundwater Potential Zone In 2024\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6898005/v1/b2a65547f0ee85c59473de0a.jpg"},{"id":85764573,"identity":"764d063e-e1b2-41f7-bb13-1f6863b23320","added_by":"auto","created_at":"2025-07-01 12:16:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":209368,"visible":true,"origin":"","legend":"\u003cp\u003ea) Changes in GWPZ Classes, b) Changes in LULC Classes related to GWPZ, c) Groundwater Depth Deviation\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6898005/v1/569749e105d1164582757308.jpg"},{"id":87196310,"identity":"fee77c0c-872b-497e-a453-19b658ced12a","added_by":"auto","created_at":"2025-07-21 12:23:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2715568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6898005/v1/a381c06b-535e-4c3b-97a5-63bdd5199fab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis on the Spatiotemporal Changes of Groundwater Potential Zone in Sylhet, Bangladesh: An AHP and GIS based approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGroundwater (GW) is the most accessed freshwater resource which is important for sustaining the health of humans and the environment by supplying water, essential nutrients, and maintaining a relatively consistent temperature [1], [2]. It gets infiltrated through the surface and gets saturated in the pores and cracks on the soils and rocks underground [3], [4]. Groundwater potential zone (GWPZ) refers to areas where there is a strong chance of finding and extracting groundwater. Identifying the groundwater potential zones has become a global issue among the hydrologists and global researchers [5]. GWPZ identification seeks to address the issue of suitable site selection for groundwater extraction for managing groundwater resources and ensuring the sustainability of groundwater use [6]. Groundwater mainly occurs because of interaction between geological, climatic, biological, physiographical, and hydrological parameters including rainfall, geology, drainage pattern, slope, soil types, etc. [7], [8], [9]. The availability of sufficient rainfall is key to enhancing groundwater resources in any area and rainfall variability can influence the infiltration of water into the aquifer system [5], [10]. However, GW resources are under stress due to anthropogenic activities, such as rapid urbanization which have led to a persistent rise in water demand and reduced permeable surfaces [11], [12], [13], [14]. To understand anthropogenic interventions, examining Land Use/Land Cover (LULC) change provides a clear and insightful perspective [15], [16].\u003c/p\u003e \u003cp\u003eIn Bangladesh, around 85% of the population relies on groundwater, with 90% of it being utilized in the agricultural sector [17], [18]. As one of the most densely populated developing countries in the world, Bangladesh is experiencing unplanned urban growth, population increase, industrial activities, and extensive agricultural practices in the shrinking cultivated land [19], [20], [21]. Such events are the main causes of altering groundwater quality and quantity in the country, including Sylhet district in the northeastern region [22], [23], [24], [25]. GIS, RS technologies, and GEE platform have widespread applications for analyzing geospatial data and natural resources [26], [27]. Utilizing GIS and RS tools to monitor GWPZ is a useful, cost and time-efficient technique [28], [29]. With the increased efficiency of satellite data, GIS, and RS, delineation of GWPZ has become more accurate and convenient in recent years [30], [31]. To demarcate GWPZ, applications of several multivariate statistical models, including frequency ratio [32], logistic regression [33], fuzzy-AHP [34], and MCDM model, named AHP [35], [36] have been widely integrated with GIS in recent decades. AHP is a highly effective MCDM method capable of determining the relative importance of thematic layers for evaluating groundwater potentiality [13], [37]. However, in terms of determining LULC, GEE is a JavaScript and python coding geospatial analysis platform which is easier, powerful, and efficient tool that provides several classification algorithms with higher accuracy that minimizes image quality problems [38], [39], [40].\u003c/p\u003e \u003cp\u003eGroundwater resources are steadily declining in Sylhet district, Bangladesh, due to human activities and environmental changes. As a result, persistent monitoring and identifying of GWPZ is necessary for effectively managing GW resources. There have been several research on evaluating the spatiotemporal variations of groundwater level and quality in the selected study area [25], [41], LULC changes [42], [43], [44], and its impact on water quality [45]. However, studies on the determination of GWPZ in the study area as well as comprehending the shifting of GWPZ combining GEE, GIS, RS, and MCDM techniques in Sylhet district haven\u0026rsquo;t been conducted yet. The findings of this study will provide 1) GWPZ mapping using nine relevant parameters in Sylhet for 1998 and 2024, as well as 2) How the three temporal variables (rainfall variability, groundwater level fluctuation, and land use land cover changes) contributed to the probable shifting of GWPZs. The insights gained from this research will help comprehend the groundwater dynamics in Sylhet support hydrologists and policymakers to comprehend groundwater stability and improve mitigation strategies in the research area.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n \u003cp\u003eSylhet district is located in the northeastern part of Bangladesh. It has total area of 3416 square kilometers and is placed between 24\u0026deg;36\u0026apos; and 25\u0026deg;11\u0026apos; north latitudes and between 91\u0026deg;38\u0026apos; and 92\u0026deg;30\u0026apos; east longitudes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Sylhet is situated downstream of the Meghalaya Hills of India. It is located within the Surma and Kushiyara floodplain, surrounded by the geographical characteristics of the hilly areas in the tertiary period, and characterized by an uneven geomorphic pattern. The primary land use in this area consists of depressions, farmlands, and settlement areas [46]. The research area lacks a central water distribution network; hence GW is the primary source of water and the majority of the area\u0026apos;s residents are supplied with water by both shallow and deep aquifers [25], [41]. The aquifers are either confined or semi-confined and is comprised of the Dupi Tilla formation\u0026rsquo;s weathered alluvial sands, young gravelly sands with mixed range of permeability [25]. The population of Sylhet District was 3,857,037 in the Bangladesh Census of 2022, with the population density of 1117 people per square kilometers (Bangladesh Bureau of Statistics Census, 2022). The enormous population places a substantial strain on groundwater resources, causing them to be overused. Understanding where groundwater potential zones are located and how it is stressed under anthropogenic intervention helps in the sustainable management of water resources. By monitoring these zones, water can be extracted in a controlled manner that prevents overexploitation and ensures long-term availability. Data from 15 observation stations were collected in the study area (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) for 1998 and 2024 for the delineation of GWPZ.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Method\u003c/h2\u003e\n \u003cp\u003eFor identifying GWPZ, nine influential thematic layers are generated and analyzed using conventional and temporal data through ArcGIS and Remote Sensing tools. The conventional layers consist of geological data, drainage density, Hydrological Soil Group (HSG) classification, lineament density, slope measurements, and Topographical Wetness Index (TWI) maps. Rainfall variability, groundwater level fluctuation, and Land Use Land Cover (LULC) map with accuracy assessment are specifically generated for 1998 and 2024. Subsequently, Analytic Hierarchy Process (AHP) method is utilized for calculating the weights for 9 parameters with a consistency ratio (CR). The generated raster of each parameter was aggregated in ArcMap to generate the GWPZ map. Additional geospatial analyses were conducted for analyzing how temporal variables impact GWPZ. The methodology illustrated sequentially in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data Source and Creation of Thematic Layers\u003c/h2\u003e\n \u003cp\u003eTraditional data for creating thematic layers are collected from multiple sources (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The SRTM Digital Elevation Model (DEM) data was extracted from the United States Geological Survey website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003c/span\u003e) with a 30-meter resolution which has been used to delineate the slope, drainage density, lineament density, and topographical wetness index using the ArcMap 10.8. Precipitation data was gathered from Bangladesh Meteorological Department\u0026rsquo;s website to determine rainfall variability for 1998 and 2024. The hydrological soil group (HSG) has been prepared from the data imported from Food and Agricultural Organization (FAO) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/\u003c/span\u003e\u003c/span\u003e), scaled at 1:5000000. The groundwater depth data was provided by Bangladesh Hydrological Department (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hydrology.bwdb.gov.bd/\u003c/span\u003e\u003c/span\u003e), which was spatially analyzed and displayed by using inverse distance weight (IDW) method in ArcMap. The Landsat 5 Thematic Mapper (TM) and Sentinel-2 Surface Reflectance (SR) Level 2A Data, were utilized to classify various land-use categories using Random Forest (RF) method in GEE platform. Random Forest (RF) Machine learning classification algorithm has been shown to surpass other methods such as maximum likelihood, Support Vector Machine (SVM), decision trees (DT) and neural networks due to its higher accuracy [47], [48], [49].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eData Sources and Details\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDetails\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUses\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation (DEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS Earth explorer\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 m resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope, Drainage Density, Lineament Density, Topographic\u003c/p\u003e\n \u003cp\u003ewetness index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBangladesh Meteorological Department (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://live8.bmd.gov.bd/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30*30 cell size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation Map (1998, 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite imagery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS Earth explorer\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e), European Space Agency (ESA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 m resolution (1998), 10 m resolution (2024)- resampled to 30*30 cell size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC map (1998, 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeological map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS Earth explorer\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30*30 cell size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeology Map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFood and Agricultural Organization (FAO)\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScale 1:5000000,\u003c/p\u003e\n \u003cp\u003e30*30 cell size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrological Soil Group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroundwater Depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBangladesh Hydrological Department\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hydrology.bwdb.gov.bd/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeters below ground level (mbgl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroundwater Level Map for 1998 and 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Accuracy Assessment for LULC Classification\u003c/h2\u003e\n \u003cp\u003eAccuracy assessment is an essential step in processing remote sensing data that evaluates the accuracy of pixels of the LULC categories [50], [51].This study implies the following formula of kappa statistical analysis to measure the accuracy of LULC.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eIn this formula, P\u003csub\u003eo\u003c/sub\u003e refers to the relative observed agreement among raters. It refers to the proportion of instances where both raters agree out of the total number of instances whereas P\u003csub\u003ee\u003c/sub\u003e indicates the hypothetical probability of chance agreement. This is the probability that the two raters would agree by chance alone. It is calculated using the marginal probabilities of each category being chosen by the raters. This statistical method is useful for assessing the reliability of subjective measurements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 AHP method and the delineation of GWPZ\u003c/h2\u003e\n \u003cp\u003eDeveloped by Saaty (1980) [52], The AHP is a hierarchically structured approach that uses deconstruction, comparison judgments, and priority synthesis to analyze and solve difficult decision-making challenges. The integration of AHP and GIS platform allows for the systematic evaluation of various factors and criteria, leveraging spatial data to make informed decisions regarding the suitability or compatibility analysis for specific purposes [53], [54]. Many researchers have combined the (AHP) with RS data and GIS technology to delineate GWPZs [55], [56]. The AHP methodology involves constructing pairwise comparison matrices, where criteria are systematically compared against each other to determine their relative ranks or weights [57], [58]. Thematic layers were converted into raster datasets, where each pixel was sized at 30 meters by 30 meters. This process assigns comparative weights according to their impact on groundwater occurrence, drawing from expert opinions and relevant literature reviews. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the pair-wise comparison matrix and normalized weights for the nine parameters. Consistency ratio (CR) indicates to the accuracy of the judgement, which must be less than or equal to 0.1. The CR is calculated from the following equation:\u003c/p\u003e\n \u003cp\u003eCR =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{\\text{CI}}{\\text{RI}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eHere, the term RI stands for the Random Consistency Index, and CI stands for the Consistency Index, calculated as shown below:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eIn this formula, 𝜆 represents the principal eigenvalue of the matrix, while n denotes the number of factors considered in the estimation (Saty 1980).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePairwise Comparison Matrix\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLayers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHSG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTWI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere, the matrix value 1 refers to the equal importance of the parameters to the objective. The values 3, 5, 7, and 9 in the AHP matrix signify the increasing importance of the layers relative to one another. Other intermediate values such as 2,4,6,8 can also be used for building the matrix. The weights allocated to the study area are determined by a combination of insights from previous literature and an analysis of the region\u0026apos;s geographical characteristics. These factors provide a comprehensive basis for the weighting process, ensuring that both historical data and the specific attributes of the area are considered. The weights, principal eigenvector, and the consistency ratio are calculated in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In the study area, rapid urbanization coupled with extensive agricultural activity makes LULC the most influential factor in delineating GWPZ. The temporal variables, including groundwater level fluctuations and average rainfall, have been assigned the following rankings. Among the fixed variables, slope is prioritized first due to the presence of steppe hills within the study area; however, it ranks fourth overall. The geological characteristics follow slope in importance, with drainage density, lineament density, and the topographical wetness index subsequently ranked. The hydrological soil group is given the least weight in the analysis, as the study area is characterized by a single, uniform soil type.\u003c/p\u003e\n \u003cp\u003eFinally, GWPI of Sylhet was generated from using reclassified raster maps of the parameters and their corresponding weights combined into the weighted overlay tool in ArcGIS Platform. Such assessments of potential groundwater recharge sites are essential requirements for effective land use planning and management. They help improve both the general situation for managing water and land resources. The GWPI equation is as follows:\u003c/p\u003e\n \u003cp\u003eGWPI = [Rr \u0026times; Rw\u0026thinsp;+\u0026thinsp;Gr \u0026times; Gw\u0026thinsp;+\u0026thinsp;Sr \u0026times; Sw\u0026thinsp;+\u0026thinsp;DDr \u0026times; DDw\u0026thinsp;+\u0026thinsp;LULCr \u0026times; LULCw\u0026thinsp;+\u0026thinsp;LDr \u0026times; LDw\u0026thinsp;+\u0026thinsp;HSGr \u0026times; HSGw\u0026thinsp;+\u0026thinsp;GLr \u0026times; GLw\u0026thinsp;+\u0026thinsp;TWIr \u0026times; TWIw]\u003c/p\u003e\n \u003cp\u003eHere, the acronyms represent R (Precipitation/ Rainfall), G (Geology), S (Slope), DD (Drainage Density), LULC (Land Use and Land Cover), LD (Lineament Density), HSG (Hydrological Soil Group), GL (Groundwater Level), TWI (Topographical Wetness Index). The superscript \u0026lsquo;w\u0026rsquo; and \u0026lsquo;r\u0026rsquo; refers to the weight of each parameter and the specific rank for each subclass of a parameter, determined by their relative importance in assessing groundwater potentiality.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWeights, and Consistency Ratio of the parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLayers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHSG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100502513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.197802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100502513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.107078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100502513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060301508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.307692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.301507538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.413793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.278481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100502513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075376884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100502513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.153846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.197802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060301508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ePrincipal Eigenvector\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e9.941\u003c/strong\u003e, Consistency Index\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e0.12\u003c/strong\u003e, Random Index for 9*9 matrix\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e1.45\u003c/strong\u003e Consistency Ratio\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e0.08\u003c/strong\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, meaning that the comparison matrix is consistent.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003eFindings on the selected parameters, groundwater potential zone delineation and how it is shifted by temporal variables are analyzed in the following texts.\u003c/p\u003e\n\u003cp\u003e3.1 Rainfall Variability\u003c/p\u003e\n\u003cp\u003eRainfall amount in a region directly influences the amount of groundwater availability, by infiltrating through the surface and recharging groundwater aquifers. Water infiltration into the aquifer system may be influenced by variations in rainfall amounts. As a result, the evaluation of the groundwater potentiality has been greatly influenced by spatiotemporal fluctuation in rainfall amounts. The annual rainfall map for 1998 and 2024 has been generated (Fig 3a, 3b) by the IDW method using the rainfall datasets from Bangladesh Meteorological Department (BMD). The northern part of Sylhet received the highest amount of rainfall for both these years, with the amount gradually declining in the south. The highest rainfall amount increased from 3129 mm/year in 1998 to 4370 mm/year in 2024, similar in terms to the lowest amount which also increased from 2200 mm/year to 2570 mm/year in 2024. The areas with higher rainfall usually contribute to rich groundwater resources.\u003c/p\u003e\n\u003cp\u003e3.2 Slope\u003c/p\u003e\n\u003cp\u003eSlope is a major factor affecting groundwater infiltration. Gentle slopes allow water to percolate into the soil, enhancing infiltration, but steep slopes lead to fast runoff and less water absorption into the aquifers [59]. Sylhet is dominated by nearly flat (0-2 degrees) and very gentle slopes (2-5 degrees). Higher sloped areas are found in central Tilla and northern regions (Fig 3c). Steppe areas are given lesser weights for groundwater potentiality. The natural break classification is used to illustrate the slope differences in the study area.\u003c/p\u003e\n\u003cp\u003e3.3 Geology\u003c/p\u003e\n\u003cp\u003eGeological mapping is essential for assessing groundwater systems, highlighting the geological structures that determine groundwater presence and flow [60]. The study area consists of a versatile range of geological features (Fig 3d), dominated by alluvial silt and clay covering over 50% of the entire area, followed by marsh clay and peat with 22.7%. The other most significant types of geology present in Sylhet are young gravelly sand and Dihing and Dhupi Tilla formations (slightly elevated terrains) with 11.7% and 7.65% areas respectively. The geological features also include formations from the Miocene, Neogene, and Oligocene eras, and weight was assigned according to their potentiality. The young gravelly sand, tipam sandstone, and the ancient geologic forms were assigned high to moderate ranks due to their properties, whereas the clay formations were assigned lower ranks for groundwater infiltration.\u003c/p\u003e\n\u003cp\u003e3.4 Drainage Density\u003c/p\u003e\n\u003cp\u003eDrainage density is the measurement of the total length of channels per unit area, reflecting how closely the channels are spaced [61]. \u0026nbsp;It is a valuable measurement of how permeable the ground is in that region, therefore serving as a useful factor for assessing groundwater dynamics and potentiality [62], [63]. Drainage density is inversely related to the permeability of a region, meaning areas with higher drainage density have higher runoff rates, leading to lower groundwater potential and vice versa [64]. The drainage density in Sylhet is classified into five categories, starting from 0.11 to 20.32 km/sq km, with the majority being dominated by areas within 8.19 sq/km (Fig 3e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.5 Lineament Density (LD)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLineaments, usually linear joints and fractures are generally formed due to tectonic stress and strain and provide valuable insights into surface features that play a crucial role in the infiltration of surface runoff into aquifers and facilitate the movement and storage of groundwater [65], [66], [67]. Areas with greater lineament density tend to have higher groundwater potential, as the increased presence of lineaments enhances surface permeability [68]. In Sylhet, lineament density starts from no lineament (0) to high level density, peaking at 1.73 km/sq km (Fig 3f). The areas having higher lineament density were assigned higher ranks and weights according to their groundwater potentiality. LD is identified using the SRTM DEM (30m) data and utilizing the hill shade and line density tool in ArcMap 10.8 platform.\u003c/p\u003e\n\u003cp\u003e3.6 Land Use Land Cover (LULC) Change Analysis\u003c/p\u003e\n\u003cp\u003eLand use and land cover change impacts the global water cycle and it\u0026rsquo;s considered as one of the most crucial roles in determining the occurrence and recharging of groundwater [69], [70]. The overuse of groundwater primarily results from the rapid expansion of agriculture and the development urban infrastructures [71], [72]. In this research, five primary LULC classes were identified in Sylhet for 1998 and 2024: water bodies, vegetation, cropland, built-up areas, and barren land (Fig 3g, 3h). LULC map for 1998 was generated from the Landsat 5 TM and Sentinel-2 datasets for 2024, utilizing the ArcMap and Google Earth Engine platforms. The land cover statistics and changing extent for both these years are given in Table 4. The central region of the district, particularly where Sylhet city is located, is predominantly characterized by built-up areas. By 2024, built-up areas had expanded by 26%, contributing to a reduction in groundwater potential due to impervious surfaces. Additionally, water bodies, both permanent and seasonal, experienced an 11% decline during this period, alongside a decrease in vegetation cover. Conversely, agricultural fields were increased by 57 square kilometers, resulting in higher groundwater extraction for meeting growing demands of the expanding population. The Kappa co-efficient accuracy values for each LULC maps are 0.83 and 0.87 respectively.\u003c/p\u003e\n\u003cp\u003eTable 4: LULC Statistics for 1998 \u0026amp; 2024\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; LULC Classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eArea in 1998 (sq_km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eArea in 2024 (sq_km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003eChange (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eRelation to Groundwater Potentiality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003eBuilt Up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e128.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e182.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e29.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003eBarren Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-99.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1701.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1758.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e784.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e759.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003eWater Bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e790.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e710.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3414.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3416.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.7 Groundwater Level (GL)\u003c/p\u003e\n\u003cp\u003eThe annual groundwater aquifer data from 15 wells were collected from Bangladesh Hydrological Department\u0026rsquo;s website for both 1998 and 2024 and visualized using the IDW interpolation technique in ArcMap 10.8 (Fig 3i, 3j). In 1998, the maximum depth at which groundwater was located was 4.62 meters below the surface, with the minimum depth recorded at 1.56 meters. By 2024, there was a marked decrease in the highest groundwater depth, which dropped to 8.85 meters, a significant change from the 4.62 meters observed in 1998. Similarly, the minimum depth where groundwater was detected also exhibited a decline, dropping to 1.77 meters below the surface. There is clear downward fluctuation in the observed groundwater level, which might be resulted from natural factors, such as the variation in rainfall, and manmade activities, such as excessive groundwater pumping, land use alterations, etc.\u003c/p\u003e\n\u003cp\u003e3.8 Hydrological Soil Group (HSG)\u003c/p\u003e\n\u003cp\u003eThe U.S. Department of Agriculture\u0026rsquo;s (USDA) Natural Resources Conservation Service (NRCS) classifies soils into four categories\u0026mdash;A, B, C, and D\u0026mdash;based on how well they absorb water when which affects their potential for groundwater recharge [73]. Table 5 shows the characteristics of these soil groups based on their infiltration characteristics.\u003c/p\u003e\n\u003cp\u003eTable 5: Hydrological Soil Group Characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eSoil Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eInfiltration Rate (inch/hour)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003ePotential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026gt;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.15-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.05-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThere are also subdivisions in these soil categories. In Sylhet, there is only type C soil, further subdivided into three categories (Fig 3k). These are C-Loam, C-Clay Loam, C-Clay. Among them, Loam has the largest area coverage with the highest groundwater potentiality among the three groups, followed by clay loam, and clay, and their weights are assigned accordingly. The HSG map was generated by the data retrieved from the Food and Agricultural Organization (FAO).\u003c/p\u003e\n\u003cp\u003eTable 6: Hydrological Soil Group Classification in Sylhet.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eHSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eArea (sq_km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eC-Loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e1763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eC-Clay Loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e1589.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e46.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eC-Clay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e62.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e3416.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.9 Topographical Wetness Index (TWI)\u003c/p\u003e\n\u003cp\u003eThe Topographical Wetness Index (TWI) indicates how the physical characteristics of the landscape\u0026mdash;specifically its shape and slope\u0026mdash;affect the way water accumulates and distributes across the surface [74]. It quantifies the relationship between upslope water flow and the steepness of the terrain. Greater TWI values indicate areas that are favorable for water accumulation and saturation. On the contrary, lower TWI values are associated with steeper slopes or ridges, where water flows quickly and is less likely to accumulate or infiltrate. The TWI value for Sylhet was delineated using the SRTM DEM (30 m) data (Figure 3l).\u003c/p\u003e\n\u003cp\u003e3.10 Evaluation of Groundwater Potential Zones and Spatiotemporal Analysis for 1998 and 2024\u003c/p\u003e\n\u003cp\u003eGPWZ\u0026rsquo;s for 1998 and 2024 (Figure 4a \u0026amp; 4b) in Sylhet was identified using nine distinct influential factors that influences groundwater occurance. Significant temporal variability, and changes in spatial extent has been observed in rainfall extent, groundwater level, and LULC classes across the study area. These temporal modifications in groundwater-controlling parameters can potentially shift the location and extent of GWPZ. The groundwater potential maps of Sylhet were categorized into five distinct classes: very low, low, moderate, high, and very high potential zones. Table 7 represents the potential zone areal statistics for 1998 and 2024. From 1998 to 2024, the groundwater potential zones (GWPZ) showed significant changes. Very low potential zones increased by 12.86%, from 18.58 sq. km (0.54%) to 21.32 sq. km (0.62%). Low potential zones experienced a minor growth of 1.91%, rising from 918.47 sq. km (26.89%) to 936.37 sq. km (27.41%). Moderate potential zones, the largest category, expanded by 4.22%, from 1,725.13 sq. km (50.50%) to 1,801.16 sq. km (52.72%). The major shift is observed for the high potential zones, which declined significantly by 15.97%, decreasing from 737.95 sq. km (21.60%) to 636.35 sq. km (18.63%). Very high potential zones exhibited slight growth, increasing by 23.53%, from 16.03 sq. km (0.47%) to 20.96 sq. km (0.61%). The detailed shifting GWPZ map is shown in Fig-5a, where negative change outweighs positive. These changes reflect dynamic shifts in groundwater potential zone, resulted from increased settlement areas and fluctuated groundwater depth, despite having higher amounts of rainfall in 2024. The GWPZ shift due to LULC class transformation and groundwater depth deviation is given in (Fig 5b, 5c).\u003c/p\u003e\n\u003cp\u003eTable 7: Areal Statistics for GWPZ in 1998 and 2024\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGWPZ Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eArea in 1998 (sq_km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003ePercantage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003eArea in 2024 (sq_km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eChange (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e18.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e12.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e918.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e26.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e936.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e27.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1725.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e50.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1801.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e52.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e737.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e21.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e636.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e18.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e15.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e16.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e20.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e23.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3416.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3416.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.11 Validation\u003c/p\u003e\n\u003cp\u003eThe evaluated GWPZ for 1998 and 2024 and it\u0026rsquo;s shifting analysis are validated against the groundwater level data for 15 wells retrieved from Bangladesh Water Development Board (BWDB). The data correlates reasonably well with trends observed with the datasets from the BWDB, showing a general decline in groundwater potential zones for 2024 in areas experiencing reduced GL.\u003c/p\u003e\n\u003cp\u003eTable 8: Validation of GWPZ\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eWell ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003eLAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003eLONG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eGL98 (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGWPZ (1998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003eGL24 (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eGWPZ 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003eGL Deviation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eGWPZ Shift on Map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9108001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e35.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePositive Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9108002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-50.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNo Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9117004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-78.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9117005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-14.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNo Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9120006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-41.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNo Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9135008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9141013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e25.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePositive Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9141015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-102.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9159018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9153012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-28.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePositive Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9153016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e25.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-137.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9141016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNo Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9162020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-91.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9162022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e91.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePositive Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGT9162024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e24.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNegative Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eBy integrating RS datasets, GIS techniques, and MCDM approach, the transformation of GWPZ in Sylhet for 1998 and 2024 has been analyzed. The reduced extent (15.92%) of high potential zone, and the increasing trend of moderate potential zone (76 square kilometers) has been notably observed in this research. The high GWPZ is primarily seen in the northern areas, immediately south to the Meghalayan foothills, and the lower GWPZ dominates on the central and southwestern parts, due to urban expansion and extensive agricultural practices coupled with lesser rainfall amounts. In recent years, the occurrence of groundwater extraction for meeting the growing demands for irrigation is posing a significant threat to groundwater storage and the sustainability of groundwater resources. Excessive extraction in areas with limited recharge capacity, might cause a rapid decline in groundwater level below the surface, potentially shifting groundwater potential zones toward lower categories. This highlights the urgency for efficient water management practices and policies for balancing agricultural needs with the preservation of groundwater resources for future generations. This methodology can be further approached by integrating various combinations of remote sensing layers to enhance the precision of identifying potential groundwater zones. By leveraging advanced geospatial techniques and incorporating additional environmental and hydrological parameters, the approach can provide more accurate and site-specific insights for sustainable groundwater management and planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eUtso Soumyo Talukdar prepared the manuscript, developed the methodology, groundwater potential zone identification, and spatiotemporal change analysis of groundwater potential zone. Milon Bokshi prepared figures 3g and 3h (Land Use Land Cover Map 1998 \u0026amp; 2024) and supervised the analysis and manuscript writing. Dr. Md. Azizul Baten and Towfiqul Islam Khan assisted on AHP matrix construction and supervised manuscript writing.\u003c/p\u003e\n\u003cp\u003eEthics and Consent to Publish declarations\u003c/p\u003e\n\u003cp\u003eWe affirm that this manuscript is original and represents our own work. It has not been previously published and is not being considered for publication by any other journal. All authors provided written consent for the publication of this article. This study did not include any human or animal participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eAll funds of the research has been self funded. All authors have reviewed and approved the manuscript, and we confirm that all those who qualify as authors are included. The authors declare that there is no competing interests regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eGroundwater level data obtained from Bangladesh Water Development Board (BWDB) and rainfall data from Bangladesh Meteorological Department: https://live8.bmd.gov.bd/.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Serele, C., P\u0026eacute;rez-Hoyos, A., \u0026amp; Kayitakire, F. (2020). Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques. Geoscience Frontiers, 11(4), 1403\u0026ndash;1413. https://doi.org/10.1016/j.gsf.2019.11.012\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Lall, U., Josset, L., \u0026amp; Russo, T. (2020). A Snapshot of the World\u0026rsquo;s Groundwater Challenges. 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Hydrological Sciences Bulletin, 24(1), 43\u0026ndash;69. https://doi.org/10.1080/02626667909491834\u003c/span\u003e\u003c/li\u003e\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":false,"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":"Groundwater Potential Zones (GWPZ), Geographic Information System (GIS), Analytical Hierarchy Process (AHP), Spatiotemporal Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6898005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6898005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater Potential Zone (GWPZ) refers to the appropriate locations for groundwater recharge and infiltration. However, groundwater resources have been constantly under stress due to rapid population increases, contributing to the increase in impermeable structures. This research article aims to analyze spatiotemporal changes in GWPZ\u0026rsquo;s in Sylhet district, Bangladesh using Remote Sensing datasets, Geographic Information System techniques, and one of the Multi-Criteria Decision Making (MCDM) models known as Analytical Hierarchy Process (AHP). Nine influential parameters, such as Rainfall amounts, Surface Geology, Slope, Land Use Land Cover (LULC) with accuracy assessment, Drainage Density, Lineament Density, Hydrological Soil Group, Groundwater Depth below surface level, and Topographical Wetness Index have been weighted by the AHP decision matrix to identify GWPZ for the years 1998 and 2024 in Sylhet district using the weighted overlay analysis in ArcGIS. The northern part of the district has shown greater potential in both years, whereas the southern and eastern regions comparatively have lower potential. The GWPZ in 2024 at Sylhet exhibits a major shift in groundwater potentiality, particularly in the high potential zone categories, which decreased by approximately 16% compared to 1998. The increasing trend of the moderate potential zones in 2024 has also been identified. Modification of the GWPZ is examined by addressing the spatiotemporal changes in the three most influential variables, which are LULC changes, Groundwater Depth Deviation, and Rainfall Variability. Validation of the research is performed with the groundwater level data collected from Bangladesh Water Development Board (BWDB). The study reflects the critical need for sustainable groundwater managing policies to alleviate the declining trend in high-potential zones and ensure water security in the region.\u003c/p\u003e","manuscriptTitle":"Analysis on the Spatiotemporal Changes of Groundwater Potential Zone in Sylhet, Bangladesh: An AHP and GIS based approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 12:08:50","doi":"10.21203/rs.3.rs-6898005/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":"682819a9-0489-4c8b-9ee7-1adb0d604369","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50664674,"name":"Earth and environmental sciences/Environmental sciences"},{"id":50664675,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-07-21T12:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 12:08:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6898005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6898005","identity":"rs-6898005","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}