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The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Author

Listed:
  • Soyoung Park

    (Geomatics Research Institute, Pukyong National University, Busan 48513, Korea)

  • Jinsoo Kim

    (Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea)

Abstract

Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

Suggested Citation

  • Soyoung Park & Jinsoo Kim, 2021. "The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2459-:d:505354
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    References listed on IDEAS

    as
    1. Brédy, Jhemson & Gallichand, Jacques & Celicourt, Paul & Gumiere, Silvio José, 2020. "Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    2. Wenya Liu & Qi Li, 2017. "An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    3. Soyoung Park & Se-Yeong Hamm & Jinsoo Kim, 2019. "Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling," Sustainability, MDPI, vol. 11(20), pages 1-20, October.
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