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

Author

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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)

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 (

Suggested Citation

  • Showmitra Kumar Sarkar & Fahad Alshehri & Shahfahad & Atiqur Rahman & Biswajeet Pradhan & Muhammad Shahab, 2025. "Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(8), pages 19799-19827, August.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:8:d:10.1007_s10668-024-04687-2
    DOI: 10.1007/s10668-024-04687-2
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