Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.

中文翻译：

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使用基于 GIS 的混合人工智能方法绘制地下水位势图

地下水是社区、农业和工业使用的主要宝贵水资源之一。在本研究中，我们开发了三种新颖的混合人工智能 (AI) 模型，它们是改进的 RealAdaBoost (MRAB)、装袋 (BA) 和旋转森林 (RF) 集成与功能树 (FT) 基分类器的组合，用于越南高地中心 DakLak 省玄武岩地形中的地下水潜力测绘 (GPM)。根据文献调查，这些提出的混合 AI 模型是新的，尚未在某个地区的 GPM 中使用。模型研究使用了地理空间技术，并使用了 130 口地下水井的地质水文数据和 12 个地形和地理环境因素。使用 One-R Attribute Evaluation 特征选择方法选择相关输入参数，用于开发 AI 模型。这些模型的性能使用各种统计指标进行评估，包括接受者操作曲线下的面积 (AUC)。结果表明，尽管本研究中开发的所有混合模型都增强了拟合优度和预测精度，但 MRAB-FT (AUC = 0.742) 模型优于 RF-FT (AUC = 0.736)、BA-FT (AUC = 0.714) ) 和单个 FT (AUC = 0.674) 模型。因此，MRAB-FT 模型可以被认为是一种用于精确 GPM 的有前途的 AI 混合技术。准确绘制地下水潜力区将有助于通过保持消耗和开发之间的平衡来充分补给含水层，以优化地下水资源的利用。