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Evaluation of machine learning algorithms for groundwater quality modeling.

Soheil Sahour1, Matin Khanbeyki2, Vahid Gholami3

  • 1Rouzbahan Institute of Higher Education, Sari, Iran.

Environmental Science and Pollution Research International
|January 30, 2023
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Summary

This study developed a machine learning framework to map groundwater quality (GWQI) in Iran, finding that the random forest model accurately predicts quality. Poor groundwater quality is widespread, primarily influenced by industrial proximity.

Keywords:
Classification algorithmsGISGWQIGroundwater quality mapMachine learning

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Machine Learning Applications

Background:

  • Traditional groundwater quality assessment via sampling and lab analysis is resource-intensive for large areas.
  • Developing efficient, scalable methods for groundwater quality mapping is crucial for water resource management.
  • Machine learning offers a promising approach to model complex relationships between groundwater quality and influencing factors.

Purpose of the Study:

  • To develop and evaluate a machine learning-based framework for mapping groundwater quality index (GWQI) in an unconfined aquifer.
  • To identify key factors controlling groundwater quality using various machine learning classifiers.
  • To provide a cost-effective methodology for groundwater quality modeling applicable to similar regions.

Main Methods:

  • Collected groundwater samples from 248 wells, measuring GWQI and classifying it into four categories.
  • Prepared spatial data for factors like distance to industry, population density, geology, and elevation in GIS.
  • Trained and evaluated six machine learning models (XGB, RF, SVM, ANN, KNN, GCM), selecting the best based on accuracy metrics (ROC, precision, recall).

Main Results:

  • The random forest (RF) model demonstrated superior performance with high accuracy (overall accuracy, precision, recall = 0.92; ROC = 0.95).
  • The RF model mapped GWQI, revealing that 'poor' quality dominates (66% of the area), followed by 'good' (19%), 'very poor' (14%), and 'excellent' (<1%).
  • Feature importance analysis identified distance to industrial centers as the primary factor influencing groundwater quality.

Conclusions:

  • Machine learning algorithms, particularly RF, provide a highly accurate and efficient method for mapping groundwater quality.
  • The developed framework offers a cost-effective alternative to traditional methods for large-scale groundwater quality assessment.
  • Findings highlight the significant impact of industrial activities on groundwater quality, informing targeted management strategies.