An Integrated Approach for Groundwater Potential Prediction Using Multi-Criteria and Heuristic Methods

This research focuses on groundwater mapping for the Çumra and Beyşehir Basins in Konya, a semi-arid region in Turkey that plays a crucial role in agriculture and the food industry. Geographic information systems (GIS), the analytical hierarchical process (AHP), and the multi-population-based differential evolution algorithm (MDE) were combined to identify potential groundwater zones. Since direct data on groundwater presence are costly to obtain, thematic maps created from groundwater conditioning factors (such as aquifer, slope, permeability, alluvial soil, soil quality, lithology, precipitation, temperature, salinity, and stone density) can be used to estimate groundwater potential. In this study, these factors were assigned weights using the AHP technique in Model 1 and the MDE technique in Model 2. The TOPSIS (technique for order preference by similarity to ideal solution) method was then employed to simulate groundwater potential, using weights from both techniques. The performance metrics of both models were as follows: Model 1 (RMSE: 114.219, MSE: 13,046.091, and MAE: 99.663) and Model 2 (RMSE: 114.209, MSE: 13,043.785, and MAE: 99.652). The proposed method addresses issues of consistency and bias that might arise from relying on expert opinions through the use of heuristic techniques. Moreover, this approach, which does not require direct data on groundwater availability, enables the creation of accurate predictions while overcoming the challenges of obtaining expensive data in underdeveloped and developing countries. It provides a scientifically sound way to identify and conserve water resources, reducing drilling and other related costs in watershed management and planning.