<p>This study uses GIS-based multi-criteria decision analysis integrated with statistical techniques to investigate groundwater potential zones (GWPZs) in Chennai, Kancheepuram, and Tiruvallur districts. The study emphasizes regional-specific assessments, identifying the coastal plain regions of Kancheepuram and Tiruvallur as areas with high groundwater potential. Lithology was identified as the most critical factor influencing groundwater availability, followed by soil and drainage density. The variability of rainfall patterns further highlighted the significance of traditional weightage methods combined with modern GIS-based modelling for accurate assessments. This integrated approach provides a robust sustainable water resource management framework, especially in urban and peri-urban areas. Future research directions include applying machine learning, enhanced geospatial techniques, and utilizing diverse datasets such as remote sensing and geophysical surveys for improved GWPZ mapping.</p> Graphical Abstract <p></p>

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Groundwater potential zone mapping and sustainable management in Chennai, Kancheepuram, and Tiruvallur districts using GIS-based multi-criteria decision analysis

  • K. Joseph Pious,
  • A. Stanley Raj

摘要

This study uses GIS-based multi-criteria decision analysis integrated with statistical techniques to investigate groundwater potential zones (GWPZs) in Chennai, Kancheepuram, and Tiruvallur districts. The study emphasizes regional-specific assessments, identifying the coastal plain regions of Kancheepuram and Tiruvallur as areas with high groundwater potential. Lithology was identified as the most critical factor influencing groundwater availability, followed by soil and drainage density. The variability of rainfall patterns further highlighted the significance of traditional weightage methods combined with modern GIS-based modelling for accurate assessments. This integrated approach provides a robust sustainable water resource management framework, especially in urban and peri-urban areas. Future research directions include applying machine learning, enhanced geospatial techniques, and utilizing diverse datasets such as remote sensing and geophysical surveys for improved GWPZ mapping.

Graphical Abstract