Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, Vietnam.
University of Transport Technology, Ha Noi, 100000, Vietnam.
Ground Water. 2021 Sep;59(5):745-760. doi: 10.1111/gwat.13094. Epub 2021 Mar 31.
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.
地下水是社区、农业和工业使用的主要有价值水资源之一。在本研究中，我们开发了三种新的混合人工智能（AI）模型，这是改进的 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，用于越南高地中心达克拉克省玄武岩地形的地下水潜力制图（GPM）。根据文献调查，这些提出的混合 AI 模型是新的，并且尚未在该地区的 GPM 中使用。本研究使用了地理空间技术和 130 个地下水井和 12 个地形和地质环境因素的地质水文数据。使用了 One-R 属性评估特征选择方法，用于选择用于 AI 模型开发的相关输入参数。使用各种统计措施（包括接收器操作曲线下的面积（AUC））评估了这些模型的性能。结果表明，尽管本研究中开发的所有混合模型都提高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）和单一 FT（AUC=0.674）模型。因此，MRAB-FT 模型可以被认为是一种有前途的 AI 混合技术，用于进行准确的地下水潜力制图。地下水潜力区的准确制图将有助于为最佳利用地下水资源进行含水层的充分补给，同时保持消耗与开采之间的平衡。
