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<p>Groundwater is a critical resource for sustainable development and agriculture in arid regions, such as the Mehran Plain, where accurate delineation of potential zones is essential. This study delineates Groundwater Potential Zones (GWPZs) by integrating GIS with four meta-heuristic optimization algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Invasive Weed Optimization (IWO). Fourteen thematic layers were weighted using each algorithm to generate GWPZ maps. The novelty of this research lies in the systematic performance comparison and dual-scenario weighting (well-density (well-d) and specific yield (Sy)) of these meta-heuristics. We validated the GWP maps against a Groundwater Flow Intensity-Direction (GFID) map. Quantitative results show that PSO_well-d achieved the highest accuracy with a spatial overlap of 80.2% and an RMSE of 0.202. The ‘Very Suitable’ zones cover 15.4% of the plain, primarily in the western sectors. The integration of GIS with meta-heuristic optimization provides an efficient, data-driven framework for groundwater resource planning in environmentally stressed regions.</p>

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GIS-based delineation of groundwater potential zones in the Mehran Plain using meta-heuristic optimization techniques

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Alireza Vafaeinejad,
  • Sasan Mahmoudi Jam,
  • Alireza Sharifi

摘要

Groundwater is a critical resource for sustainable development and agriculture in arid regions, such as the Mehran Plain, where accurate delineation of potential zones is essential. This study delineates Groundwater Potential Zones (GWPZs) by integrating GIS with four meta-heuristic optimization algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Invasive Weed Optimization (IWO). Fourteen thematic layers were weighted using each algorithm to generate GWPZ maps. The novelty of this research lies in the systematic performance comparison and dual-scenario weighting (well-density (well-d) and specific yield (Sy)) of these meta-heuristics. We validated the GWP maps against a Groundwater Flow Intensity-Direction (GFID) map. Quantitative results show that PSO_well-d achieved the highest accuracy with a spatial overlap of 80.2% and an RMSE of 0.202. The ‘Very Suitable’ zones cover 15.4% of the plain, primarily in the western sectors. The integration of GIS with meta-heuristic optimization provides an efficient, data-driven framework for groundwater resource planning in environmentally stressed regions.