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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
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.
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.
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