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Using machine learning algorithms to map the groundwater recharge potential zones.

Hamid Reza Pourghasemi1, Nitheshnirmal Sadhasivam2, Saleh Yousefi3

  • 1Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.

Journal of Environmental Management
|April 11, 2020
PubMed
Summary

Machine learning algorithms accurately map groundwater recharge potential. The Random Forest algorithm demonstrated superior performance, providing valuable guidance for sustainable freshwater management in arid regions.

Keywords:
Groundwater rechargeLASSOMachine learning algorithmsVariable importance

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Area of Science:

  • Hydrology
  • Environmental Science
  • Data Science

Background:

  • Sustainable freshwater management is critical, particularly in arid regions.
  • Groundwater recharge is a key component of sustainable water resource management.
  • Machine learning offers advanced tools for analyzing hydrological data.

Purpose of the Study:

  • To evaluate machine learning algorithms for generating groundwater recharge potential maps (GRPMs).
  • To compare the predictive accuracy of Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), and Random Forest (RF) algorithms.
  • To identify the most effective algorithm for mapping groundwater recharge potential.

Main Methods:

  • Utilized sixteen effective factors including elevation, rainfall, lithology, and land use to generate GRPMs.
  • Employed SVM, MARS, and RF machine learning algorithms for spatial prediction.
  • Assessed variable importance using the LASSO algorithm and validated GRPMs using ROC-AUC and other statistical metrics.

Main Results:

  • The Random Forest (RF) algorithm achieved the highest accuracy (AUC = 0.987), outperforming SVM (AUC = 0.963) and MARS (AUC = 0.962).
  • All tested algorithms demonstrated excellent predictive accuracy based on ROC curve thresholds.
  • Variable importance analysis identified key factors influencing groundwater recharge potential.

Conclusions:

  • Machine learning algorithms, particularly RF, are highly effective for generating accurate groundwater recharge potential maps.
  • GRPMs generated through this study serve as valuable tools for policymakers in sustainable groundwater management.
  • The findings highlight the potential of integrating advanced computational methods into hydrological studies.