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<p>Groundwater (GW) represents a critical resource for sustaining agriculture and rural communities across the arid regions of many developing countries. This study assesses three predictive approaches boosted classification tree (BCT), the bivariate frequency ratio (FR), and a hybrid BCT–FR ensemble for mapping Potential Zones (GWPZ) in arid environments. The modelling framework integrates satellite-derived variables with pumping-test measurements (specific capacity (SPC) and transmissivity (T)) and incorporates topographic, geological, hydrogeological, and anthropogenic factors using an inventory of forty-two wells divided into calibration (70%) and validation (30%) datasets across the West El-Minia region of Upper Egypt. Change-detection analysis over the study period (2000–2025) indicated a substantial increase in agricultural activity, with cultivated lands expanding by more than 560&#xa0;km². This expansion was accompanied by an observed level decline of approximately five meters over the same period, based on field measurements from 42 wells and calculated using observed water table differences. Based on SPC predictions, the BCT and hybrid FR–BCT models achieved relatively high area under the curve (AUC) values. For transmissivity, the corresponding accuracy values were 83.38% for BCT and 92.58% for FR–BCT. Model outputs were assessing their reliability by comparing the GW potential map generated with available borehole information and daily GWproductivity data from the aquifer system. The study area was classified into four GW potential categories: very high (8%), high (26%), moderate (54%), and low (12%), with the northeastern sector exhibiting the highest recharge and storage potential. Overall, the applied machine-learning techniques demonstrated good performance for GW potential assessment in data-limited environments. The results provide important guidance for GW resource management by identifying zones with substantial recharge and development potential.</p>

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Advancing sustainable groundwater mapping and management in arid quaternary aquifers using machine learning and geospatial analytics integrating remote sensing and field hydrogeological data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Abd El-Hay A. Farrag,
  • Nourham M. Sayed,
  • Amnah Aldohan,
  • Ahmed Gad,
  • Youssef M. Youssef,
  • Hanaa A. Megahed

摘要

Groundwater (GW) represents a critical resource for sustaining agriculture and rural communities across the arid regions of many developing countries. This study assesses three predictive approaches boosted classification tree (BCT), the bivariate frequency ratio (FR), and a hybrid BCT–FR ensemble for mapping Potential Zones (GWPZ) in arid environments. The modelling framework integrates satellite-derived variables with pumping-test measurements (specific capacity (SPC) and transmissivity (T)) and incorporates topographic, geological, hydrogeological, and anthropogenic factors using an inventory of forty-two wells divided into calibration (70%) and validation (30%) datasets across the West El-Minia region of Upper Egypt. Change-detection analysis over the study period (2000–2025) indicated a substantial increase in agricultural activity, with cultivated lands expanding by more than 560 km². This expansion was accompanied by an observed level decline of approximately five meters over the same period, based on field measurements from 42 wells and calculated using observed water table differences. Based on SPC predictions, the BCT and hybrid FR–BCT models achieved relatively high area under the curve (AUC) values. For transmissivity, the corresponding accuracy values were 83.38% for BCT and 92.58% for FR–BCT. Model outputs were assessing their reliability by comparing the GW potential map generated with available borehole information and daily GWproductivity data from the aquifer system. The study area was classified into four GW potential categories: very high (8%), high (26%), moderate (54%), and low (12%), with the northeastern sector exhibiting the highest recharge and storage potential. Overall, the applied machine-learning techniques demonstrated good performance for GW potential assessment in data-limited environments. The results provide important guidance for GW resource management by identifying zones with substantial recharge and development potential.