Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods |
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| Authors: | Tran Van Phong Binh Thai Pham Phan Trong Trinh Hai-Bang Ly Quoc Hung Vu Lanh Si Ho Hiep Van Le Lai Hop Phong Mohammadtaghi Avand Indra Prakash |
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| Institution: | 1. Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, Vietnam;2. University of Transport Technology, Ha Noi, 100000 Vietnam;3. Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, 100000 Vietnam;4. Department of Watershed Management Engineering and Sciences, Faculty of Natural Resources and Marine Science, Tarbiat Modares University, Tehran, Iran;5. DDG (R) Geological Survey of India, Gandhinagar, 382010 India |
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| Abstract: | 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. |
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