ABSTRACT Due to climate change and urban growth, the demand for new freshwater sources, especially groundwater, is increasing in water-deficient countries like Iran. Therefore, this study aimed at groundwater potential mapping (GPM) of the Nahavand Plain, Iran, using an optimized adaptive neuro fuzzy inference system (ANFIS) in a geographic information system, with three metaheuristic optimization algorithms: differential evolution (DE), particle swarm optimization (PSO) and ant colony optimization (ACO). A spatial database was constructed using 273 spring locations and 14 groundwater conditioning factors. The optimization algorithms were evaluated using the receiver operating characteristic (ROC) technique. The ANFIS-DE, ANFIS-PSO and ANFIS-ACO models resulted in accuracy of 0.816, 0.809 and 0.758, respectively; the high and very high potential for groundwater springs covered 26% of the Nahavand Plain. The Root Mean Square Error (RMSE) for the training and validation datasets was lowest for the ANFIS-DE model compared to the other two models; and the ANFIS-PSO model had a higher convergence speed. These results may play an important role in sustainable groundwater management in the Nahavand Plain.

中文翻译：

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使用元启发式方法改进地下水潜力图

摘要 由于气候变化和城市发展，伊朗等缺水国家对新的淡水资源，尤其是地下水的需求正在增加。因此，本研究针对伊朗纳哈万德平原的地下水位图（GPM），在地理信息系统中使用优化的自适应神经模糊推理系统（ANFIS），采用三种元启发式优化算法：差分进化（DE）、粒子群优化（PSO）和蚁群优化（ACO）。使用 273 个泉水位置和 14 个地下水调节因子构建了一个空间数据库。使用接收器操作特性 (ROC) 技术评估优化算法。ANFIS-DE、ANFIS-PSO 和 ANFIS-ACO 模型的准确度分别为 0.816、0.809 和 0.758；地下水泉的高潜力和非常高的潜力覆盖了纳哈万德平原的 26%。与其他两个模型相比，ANFIS-DE 模型的训练和验证数据集的均方根误差 (RMSE) 最低；并且ANFIS-PSO模型具有更高的收敛速度。这些结果可能在 Nahavand 平原的可持续地下水管理中发挥重要作用。