Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.

中文翻译：

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使用自学习贝叶斯网络模型的地下水潜力测绘：元启发式算法之间的比较

由于地表水资源的减少和干旱的频繁发生，地下水资源的开采面临着严峻的挑战。为了对这些宝贵资源进行最佳管理，必须认真研究地下水的潜在状况。这项研究的主要目标是使用三种元启发式优化算法-遗传算法（GA），模拟退火（SA）算法和禁忌搜索（ TS）算法-准备地下水位图。该方法已应用于伊朗胡塞斯坦省的Baghmalek镇。为了进行建模，研究区域中有187个弹簧的位置和13个参数（高度，坡度角，坡度，平面曲率，轮廓曲率，地形湿度指数（TWI），提供了到河流的距离，到断层的距离，排水密度，降雨，土地利用/覆盖率，岩性和土壤），这些都影响地下水的潜力。此外，使用确定性因子（CF）的统计方法确定混合模型的输入权重。OneR技术的结果表明，与其他参数相比，海拔，岩性和排水密度等参数对于地下水的潜力更为重要。使用BayesNet-GA，BayesNet-SA和BayesNet的曲线下的接收器工作特征（ROC）区域（AUC）进行的地下水电势测绘（GPM）结果显示，估算精度为0.830、0.818、0.810和0.792 -TS和BayesNet模型。BayesNet-GA模型提高了BayesNet-SA的GPM估算精度（分别为4.6％和7）。5％）和BayesNet-TS（21.8％和17.5％）模型分别针对均方根误差（RMSE）和平均绝对误差（MAE）。基于度量指标，在确定GPM方面，遗传算法提供了比SA和TS算法更高的能力来优化BayesNet模型。