Groundwater potential mapping in Jashore, Bangladesh

Fatema, Kaniz; Joy, Md. Ashikur Rahman; Amin, F.M. Rezvi; Sarkar, Showmitra Kumar

doi:10.1016/j.heliyon.2023.e13966

Cited by 19 publications

(2 citation statements)

References 70 publications

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“…The results shown that high groundwater resources were situated in close vicinity to the river channel (<500 m), characterized by a gentle slope, high TWI, high soil moisture, and low drainage density (<2.2 sq km) around the riverbank area [ 71 ]. showed that slope, LULC, and soil factors were the most sensitive factors for assessing GWPZ in Bangladesh.…”

Section: Discussionmentioning

confidence: 99%

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Singha,

Swain,

Pradhan

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Singha,

Swain,

Pradhan

et al. 2024

Heliyon

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“…The approach helps discover high-potential groundwater sites for efficient and sustainable agriculture while limiting overexploitation in sensitive locations. Mapping helps manage water supplies in highly populated regions to fulfil household needs 54 . Mapping and zoning are also necessary for informed decision-making due to climatic vulnerabilities, salt intrusion problems, and groundwater quality difficulties.…”

Section: Introductionmentioning

confidence: 99%

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

Self Cite

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The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

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Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

Ehsan,

Shabbir,

Al-Quraishi

et al. 2024

Appl Water Sci

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Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.

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Groundwater potential mapping in Jashore, Bangladesh

Cited by 19 publications

References 70 publications

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

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