Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

Duong Hai Ha (), Phong Tung Nguyen (), Romulus Costache (), Nadhir Al-Ansari (), Tran Phong (), Huu Duy Nguyen (), Mahdis Amiri (), Rohit Sharma (), Indra Prakash (), Hiep Le, Hanh Bich Thi Nguyen and Binh Thai Pham ()
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Duong Hai Ha: Institute for Water and Environment
Phong Tung Nguyen: Vietnam Academy for Water Resources
Romulus Costache: Danube Delta National Institute for Research and Development
Nadhir Al-Ansari: Lulea University of Technology
Tran Phong: Vietnam Academy of Sciences and Technology
Huu Duy Nguyen: VNU University of Science, Vietnam National University
Mahdis Amiri: Gorgan University of Agricultural Sciences & Natural Resources
Rohit Sharma: SRM Institute of Science and Technology
Indra Prakash: DDG (R) Geological Survey of India
Hiep Le: University of Transport Technology
Hanh Bich Thi Nguyen: University of Transport Technology
Binh Thai Pham: University of Transport Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 13, No 8, 4415-4433

Abstract: 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: Groundwater potential mapping; GIS; Sustainable groundwater management; Machine learning; Hybrid models (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11269-021-02957-6

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