|
|||||
|
|
Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping |
|
| Authors: | Ha Duong Hai Nguyen Phong Tung Costache Romulus Al-Ansari Nadhir Van Phong Tran Nguyen Huu Duy Amiri Mahdis Sharma Rohit Prakash Indra Van Le Hiep Nguyen Hanh Bich Thi Pham Binh Thai |
| Affiliation: | 1.Institute for Water and Environment, Hanoi, 100000, Vietnam ;2.Vietnam Academy for Water Resources, Hanoi, 100000, Vietnam ;3.Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania ;4.Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania ;5.Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, Bucharest, Romania ;6.National Institute of Hydrology and Water Management, Bucure?ti-Ploie?ti Road, 97E, 1st District, 013686, Bucharest, Romania ;7.Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87, Lule?, Sweden ;8.Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, 100000, Vietnam ;9.Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi, 100000, Vietnam ;10.Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural Resources, 4918943464, Gorgan, Iran ;11.Department of Electronics & Communication Engineering, SRM Institute of Science and Technology, Ghaziabad, 201204, India ;12.DDG (R) Geological Survey of India, Gandhinagar, 382010, India ;13.University of Transport Technology, Hanoi, 100000, Vietnam ; |
| Abstract: | In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC?=?0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources. |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ | |