Sustainable Groundwater Management in Water-Scarce Regions: A Spatial Machine Learning Analysis from Rajshahi, Bangladesh

Authors

  • Sumaya Tabassum

    Department of Civil Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
  • Likhon Chandra Roy

    Department of Civil Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
  • Amit Kumar Sarkar

    Department of Public Health Engineering, Government of the People’s Republic of Bangladesh, Chapainawabganj

    6300, Bangladesh
  • Yassine Ezaier

    Bio-Geosciences and Materials Engineering Laboratory, Ecole Normale Supérieure, University Hassan II, Casablanca

    20100, Morocco
  • Hader Ahmed

    Bio-Geosciences and Materials Engineering Laboratory, Ecole Normale Supérieure, University Hassan II, Casablanca

    20100, Morocco
  • Lghazi Youssef

    Bio-Geosciences and Materials Engineering Laboratory, Ecole Normale Supérieure, University Hassan II, Casablanca 20100, Morocco
  • Hesam Kamyab

    Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600077, India

    The KU-KIST Graduate School of Energy and Environment, Korea University, Seoul 02841, Republic of Korea
  • Hussameldin Ibrahim

    Clean Energy Technologies Research Institute (CETRI), Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
  • Mohammad Yusuf

    Clean Energy Technologies Research Institute (CETRI), Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada

    Architecture Department, Faculty of Architecture and Urbanism, UTE University, Quito 170527, Ecuador

DOI:

https://doi.org/10.30564/re.v7i3.10453
Received: 11 June 2025 | Revised: 23 June 2025 | Accepted: 17 July 2025 | Published Online: 12 August 2025

Abstract

Ensuring the availability and sustainable management of water (SDG 6) is particularly challenging in dry regions like Rajshahi, Bangladesh, where communities rely heavily on groundwater with limited recharge potential. Issues such as declining water levels and contamination by iron, arsenic, and chloride compromise both user satisfaction and public health. This study aimed to assess groundwater quality risks through regional mapping to guide the installation depth of new water sources. In collaboration with the Department of Public Health Engineering (DPHE), data were collected from 7,388 tube wells across nine upazilas, including well depth, geographic coordinates, and contaminant concentrations. Water quality was evaluated against World Health Organization and Bangladesh standards. Machine learning (XGBoost) and spatial analysis were applied to model contaminant levels based on location and well depth. An initial model showed poor performance, but after identifying and correcting key errors, the refined model yielded significant improvements: R² increased from 0.0345 to 0.62 for iron, from −0.0015 to 0.38 for arsenic, and from 0.12 to 0.71 for chloride. A comprehensive water quality risk map was developed by integrating these results at the upazila level. This map provides actionable insights for government agencies and NGOs to prioritize areas for water quality testing, remediation, and public awareness initiatives, contributing to more informed and sustainable water resource management in the region.

Keywords:

Arsenic Contamination; SDG 6; Iron Contamination; Health Risk; Groundwater Accessibility

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How to Cite

Tabassum, S., Chandra Roy, L., Kumar Sarkar, A., Ezaier, Y., Ahmed, H., Youssef, L., Kamyab, H., Ibrahim, H., & Yusuf, M. (2025). Sustainable Groundwater Management in Water-Scarce Regions: A Spatial Machine Learning Analysis from Rajshahi, Bangladesh. Research in Ecology, 7(3), 268–286. https://doi.org/10.30564/re.v7i3.10453

Issue

Vol. 7, Iss. 3 (August 2025)(in progress)

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Article

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Copyright © 2025 Sumaya Tabassum, Likhon Chandra Roy, Amit Kumar Sarkar, Yassine Ezaier, Hader Ahmed, Lghazi Youssef, Hesam Kamyab, Hussameldin Ibrahim, Mohammad Yusuf

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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