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<p>Effective groundwater monitoring is essential for sustainable plain management. This study proposes a geographic information system (GIS)-based optimization framework for redesigning representative well networks, demonstrated in the Arak Plain, Iran. Hydrogeological indicators were integrated into GIS to produce a groundwater suitability map. Wells’ suitability values were classified using the spatial “k”luster analysis by tree edge removal (SKATER) algorithm, delineating nine hydrogeological zones. A linear programming model optimized the number of monitoring wells per zone, incorporating proportionality criteria of zonal discharge, area, and discharge density ratios, with tolerance thresholds defined. Results revealed spatial imbalances in the existing network: high-stress zones, particularly zone 8 were under-monitored, while low-stress zones such as zone 7 were oversampled. Optimization reallocated monitoring wells, decreasing numbers in oversampled zones and increasing them in under-monitored ones, leading to a balanced network. Monitoring wells were further selected using two approaches: the multi-criteria technique for order of preference by similarity to ideal solution (TOPSIS) method and a genetic algorithm (GA). The GA achieved lower mean absolute percentage errors for annual abstraction (12.9 vs. 21.3%) and groundwater-level decline (3.2 vs. 7.6%) and identified more sites, particularly in high-stress zones. However, TOPSIS aligned better with institutional priorities due to greater overlap with existing wells. A hybrid strategy, adopting TOPSIS for immediate implementation while integrating GA-identified sites in stressed zones balances operational feasibility with improved representativeness. Overall, the framework integrates hydrogeological zoning with optimization models to enhance representativeness and functional accuracy of groundwater monitoring, providing a transferable methodology for redesigning plain monitoring worldwide.</p>

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A GIS-based multi-criteria and evolutionary optimization framework for groundwater monitoring network design and well selection

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Nazanin Shahkarami,
  • Sarvin Chitsaz

摘要

Effective groundwater monitoring is essential for sustainable plain management. This study proposes a geographic information system (GIS)-based optimization framework for redesigning representative well networks, demonstrated in the Arak Plain, Iran. Hydrogeological indicators were integrated into GIS to produce a groundwater suitability map. Wells’ suitability values were classified using the spatial “k”luster analysis by tree edge removal (SKATER) algorithm, delineating nine hydrogeological zones. A linear programming model optimized the number of monitoring wells per zone, incorporating proportionality criteria of zonal discharge, area, and discharge density ratios, with tolerance thresholds defined. Results revealed spatial imbalances in the existing network: high-stress zones, particularly zone 8 were under-monitored, while low-stress zones such as zone 7 were oversampled. Optimization reallocated monitoring wells, decreasing numbers in oversampled zones and increasing them in under-monitored ones, leading to a balanced network. Monitoring wells were further selected using two approaches: the multi-criteria technique for order of preference by similarity to ideal solution (TOPSIS) method and a genetic algorithm (GA). The GA achieved lower mean absolute percentage errors for annual abstraction (12.9 vs. 21.3%) and groundwater-level decline (3.2 vs. 7.6%) and identified more sites, particularly in high-stress zones. However, TOPSIS aligned better with institutional priorities due to greater overlap with existing wells. A hybrid strategy, adopting TOPSIS for immediate implementation while integrating GA-identified sites in stressed zones balances operational feasibility with improved representativeness. Overall, the framework integrates hydrogeological zoning with optimization models to enhance representativeness and functional accuracy of groundwater monitoring, providing a transferable methodology for redesigning plain monitoring worldwide.