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

Author

Listed:
  • 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
    Transilvania University of Brasov
    Research Institute of the University of Bucharest
    National Institute of Hydrology and Water Management)

  • 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)

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.

Suggested Citation

  • 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 & Binh Thai Pham, 2021. "Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4415-4433, October.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:13:d:10.1007_s11269-021-02957-6
    DOI: 10.1007/s11269-021-02957-6
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