Groundwater being an essential resource is not easily available in some parts of the world. The present study, aimed at procuring better prediction maps for groundwater potential zones, is based on a novel approach combining the use of k-fold cross-validation method and the implementation of four scenarios, each comprising of six machine learning models, ANFIS (Adaptive Neuro Fuzzy Inference System) and five other ensembles of it, ANFIS-Firefly, ANFIS-Bees, ANFIS-GA, ANFIS-DE and ANFIS-ACO. Ada Boost Model has played a vital role in determining the collinearity among the fourteen conditioning factors, which are, Lithology, Slope, TST, TRI, LULC, HAND, Curvature, Distance to Stream, Distance to Fault, Rainfall, Fault Density, Drainage Density, Elevation and Aspect. The AUCROC (Area Under Curve – Receiver Operating Characteristics) approach was employed as a model evaluation metric along with Accuracy, Sensitivity and Specificity. Among the models, ANFIS-DE showed the most promising results, acquiring the highest average values among the four scenarios for AUC (0.934), Accuracy (0.987), Sensitivity (0.985) and Specificity (0.985). Promising results of this study gives the necessary incentive for further applying this approach for groundwater zonation of other areas of the world as well as other areas of hydrogeological studies.

地下水是必不可少的资源，在世界某些地区并不容易获得。本研究旨在为地下水潜在区获得更好的预测图，该研究基于一种新颖的方法，该方法结合了k折交叉验证方法的使用和四种方案的实现，每种方案均包含六个机器学习模型ANFIS（自适应神经模糊推理系统）和它的其他五个集合，即ANFIS-萤火虫，ANFIS-蜜蜂，ANFIS-GA，ANFIS-DE和ANFIS-ACO。Ada Boost模型在确定十四种条件因素之间的共线性中起着至关重要的作用，这些因素包括岩性，坡度，TST，TRI，LULC，HAND，曲率，到溪流的距离，到断层的距离，降雨，断层密度，排水密度，高程和长宽比。AUCROC（曲线下面积-接收器工作特性）方法与准确性，敏感性和特异性一起被用作模型评估指标。在这些模型中，ANFIS-DE显示出最有希望的结果，在AUC（0.934），准确性（0.987），灵敏度（0.985）和特异性（0.985）的四种情况下获得最高的平均值。这项研究的有希望的结果为进一步将这种方法应用于世界其他地区以及水文地质研究的其他地区的地下水分区提供了必要的动力。