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<p>Groundwater resources in the southern region of Delhi are under severe stress due to rapid urbanization, excessive abstraction, and declining recharge. This study delineated groundwater potential zones (GWPZs) by comparing a conventional Analytical Hierarchy Process (AHP) model with four data-driven approaches: XGBoost, Random Forest (RF), Multilayer Perceptron Neural Network (MLPNN), and Deep Learning Neural Network (DLNN). Thirteen conditioning factors representing geology, geomorphology, hydrology, terrain, and land-surface characteristics were integrated within a common GIS-based framework. Model performance was evaluated using internal hold-out ROC-AUC analysis and threshold-based confusion matrices and was further examined through independent field validation using 51 georeferenced wells. The DLNN model achieved the highest predictive accuracy (AUC = 0.932), followed by MLPNN (0.901), XGBoost (0.891), RF (0.871), and AHP (0.824), while external validation preserved the same overall ranking. High to Very High groundwater potential zones were concentrated mainly along the Yamuna floodplain and favorable alluvial sectors, whereas the central and southwestern hard-rock and densely urbanized areas were dominated by lower potential classes. The study demonstrates that data-driven models, particularly DLNN and MLPNN, provide more reliable delineation of GWPZs than the knowledge-driven AHP baseline and that independent field validation strengthens the practical value of the generated maps for groundwater management and recharge planning in rapidly urbanizing environments.</p>

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Comparative evaluation of AHP and advanced machine learning models for delineating groundwater potential zones in Southern region of Delhi, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Deepanshi Tanwar,
  • Kiranmay Sarma,
  • Sayantan Mandal

摘要

Groundwater resources in the southern region of Delhi are under severe stress due to rapid urbanization, excessive abstraction, and declining recharge. This study delineated groundwater potential zones (GWPZs) by comparing a conventional Analytical Hierarchy Process (AHP) model with four data-driven approaches: XGBoost, Random Forest (RF), Multilayer Perceptron Neural Network (MLPNN), and Deep Learning Neural Network (DLNN). Thirteen conditioning factors representing geology, geomorphology, hydrology, terrain, and land-surface characteristics were integrated within a common GIS-based framework. Model performance was evaluated using internal hold-out ROC-AUC analysis and threshold-based confusion matrices and was further examined through independent field validation using 51 georeferenced wells. The DLNN model achieved the highest predictive accuracy (AUC = 0.932), followed by MLPNN (0.901), XGBoost (0.891), RF (0.871), and AHP (0.824), while external validation preserved the same overall ranking. High to Very High groundwater potential zones were concentrated mainly along the Yamuna floodplain and favorable alluvial sectors, whereas the central and southwestern hard-rock and densely urbanized areas were dominated by lower potential classes. The study demonstrates that data-driven models, particularly DLNN and MLPNN, provide more reliable delineation of GWPZs than the knowledge-driven AHP baseline and that independent field validation strengthens the practical value of the generated maps for groundwater management and recharge planning in rapidly urbanizing environments.