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Prediction of groundwater quality using efficient machine learning technique.

Sudhakar Singha1, Srinivas Pasupuleti1, Soumya S Singha1

  • 1Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, Jharkhand, India.

Chemosphere
|June 5, 2021
PubMed
Summary

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A deep learning (DL) model accurately predicts groundwater quality, outperforming other machine learning (ML) methods. This advancement is crucial for managing water resources and ensuring safe drinking water supplies.

Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Understanding groundwater quality is essential for safe drinking water and effective water management.
  • Pollution levels in groundwater require accurate prediction for proactive control measures.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model for predicting groundwater quality.
  • To compare the DL model's performance against traditional machine learning (ML) models: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN).

Main Methods:

  • Collected 226 groundwater samples from an agriculturally intensive area in Raipur district, Chhattisgarh, India.
  • Measured physicochemical parameters to calculate the entropy weight-based groundwater quality index (EWQI).
Keywords:
Entropy weight-based groundwater quality indexMachine learning algorithmsPrediction modelsVariable importance

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  • Utilized five error metrics to assess prediction performance across all models.

    • Main Results:

    • The DL model achieved the highest prediction accuracy with an R² of 0.996.
    • Performance comparison: DL (0.996) > XGBoost (0.927) > ANN (0.917) > RF (0.886).
    • DL model's output uncertainty was validated through repeated runs with randomized datasets, showing minimal deviation.

    Conclusions:

    • Deep learning presents the most realistic and accurate approach for predicting groundwater quality.
    • The developed DL model offers a significant improvement in water quality prediction accuracy.
    • Findings support enhanced groundwater management strategies through reliable water quality forecasting.