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Comparative Analysis of Machine Learning Models in Groundwater Quality Assessment: A Systematic Review.

Ankesh Kumar1, Prasoon Kumar Singh1, Vivek Kumar1

  • 1Department of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India.

Water Environment Research : a Research Publication of the Water Environment Federation
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models significantly enhance groundwater quality (GWQ) assessment by identifying complex pollution patterns. Neural networks are the most common ML approach, with emerging techniques showing promise for future GWQ management.

Keywords:
groundwater quality modellingmachine learningsupervised MLwater pollution level

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Groundwater quality (GWQ) is a critical global issue due to agricultural and industrial impacts, with groundwater supplying half of the world's drinking water.
  • Traditional water quality monitoring methods are limited in providing comprehensive trend analysis, highlighting the need for advanced predictive tools.
  • Machine learning (ML) offers powerful capabilities for analyzing complex data patterns to predict GWQ trends accurately.

Purpose of the Study:

  • To conduct a meta-analysis and bibliographic review of ML applications in GWQ assessment.
  • To evaluate the performance of various ML models, including artificial neural networks (ANN), support vector machines (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and deep learning (DL).
  • To identify current trends, research gaps, and future directions in ML-based GWQ modeling.

Main Methods:

  • Bibliographic review and meta-analysis of scientific literature on ML applications in GWQ.
  • Categorization and evaluation of different ML algorithms based on accuracy, applicability, and usability.
  • Analysis of geographical distribution of studies and commonly modeled GWQ parameters.

Main Results:

  • Artificial neural networks (ANN) are the most frequently utilized ML models in GWQ assessment.
  • India, China, and the United States lead in groundwater modeling research, supported by substantial historical data.
  • Nitrate and heavy metal pollution are the most studied GWQ parameters, appearing in approximately 50% of the reviewed studies.

Conclusions:

  • Significant progress has been made in applying ML to GWQ, but research gaps persist, especially for under-explored parameters.
  • Emerging ML techniques like physics-informed machine learning (PIML), graph neural networks (GNNs), transformer architectures, and large language models (LLMs) hold substantial potential.
  • Future research should focus on integrating advanced ML techniques for more robust and comprehensive GWQ management frameworks.