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

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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.

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Acknowledgements

We thank to Vietnam Academy for Water Resources for providing the data to carry out this research.

Funding

This research is funded by Vietnam Ministry of Science Technology under the state project Code ĐTĐL.CN-65/15. 

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Authors and Affiliations

  1. Institute for Water and Environment, Hanoi, 100000, Vietnam

    Duong Hai Ha

  2. Vietnam Academy for Water Resources, Hanoi, 100000, Vietnam

    Phong Tung Nguyen

  3. Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania

    Romulus Costache

  4. Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania

    Romulus Costache

  5. Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, Bucharest, Romania

    Romulus Costache

  6. National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, Bucharest, Romania

    Romulus Costache

  7. Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87, Luleå, Sweden

    Nadhir Al-Ansari

  8. Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, 100000, Vietnam

    Tran Van Phong

  9. Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi, 100000, Vietnam

    Huu Duy Nguyen

  10. Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural Resources, 4918943464, Gorgan, Iran

    Mahdis Amiri

  11. Department of Electronics & Communication Engineering, SRM Institute of Science and Technology, Ghaziabad, 201204, India

    Rohit Sharma

  12. DDG (R) Geological Survey of India, Gandhinagar, 382010, India

    Indra Prakash

  13. University of Transport Technology, Hanoi, 100000, Vietnam

    Hiep Van Le, Hanh Bich Thi Nguyen & Binh Thai Pham

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  1. Duong Hai Ha
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  2. Phong Tung Nguyen
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Correspondence to Duong Hai Ha, Romulus Costache or Binh Thai Pham.

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Ha, D.H., Nguyen, P.T., Costache, R. et al. Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping. Water Resour Manage 35, 4415–4433 (2021). https://doi.org/10.1007/s11269-021-02957-6

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