Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh

Showmitra Kumar Sarkar (), Fahad Alshehri (), Shahfahad (), Atiqur Rahman (), Biswajeet Pradhan () and Muhammad Shahab ()
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Showmitra Kumar Sarkar: Khulna University of Engineering and Technology
Fahad Alshehri: Geology and Geophysics Department, King Saud University
Shahfahad: Jamia Millia Islamia
Atiqur Rahman: Jamia Millia Islamia
Biswajeet Pradhan: University of Technology Sydney
Muhammad Shahab: Geology and Geophysics Department, King Saud University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 8, No 74, 19799-19827

Abstract: Abstract A severe threat to natural resources and human livelihood is groundwater scarcity. Therefore, mapping groundwater potentiality (GWP) is necessary for future resource management. In this article, a framework for conducting ensemble modeling is introduced. This framework is used to map GWP at the national level under the scenario of climatic variability. Thirteen elements linked to topography, geology, hydrology, and land cover, as well as six climatic indicators based on historical time series data, were used to map the GWP. This study has used three conventional machine learning algorithms (

Keywords: Groundwater potentiality mapping; Climate change; Machine learning techniques; Logistic regression; Stacking algorithm; Bangladesh (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-024-04687-2

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