Advancing groundwater quality predictions: Machine learning challenges and solutions.

Area of Science:

• Environmental Science
• Data Science
• Hydrogeology

Background:

• Machine learning (ML) is increasingly vital for predicting and managing groundwater contamination.
• A comprehensive review of 230 papers highlights the evolution and application of ML in environmental science.
• Understanding current ML applications in groundwater quality research is essential for future advancements.

Purpose of the Study:

• To critically review the advancements and challenges of ML in groundwater quality research.
• To identify gaps in current ML methodologies, including preprocessing and interpretability.
• To provide a framework for establishing minimum standards in ML for groundwater studies.

Main Methods:

• Systematic literature review of 230 research papers on ML and groundwater quality.
• Analysis of ML implementation, focusing on preprocessing, optimization, and interpretability.
• Evaluation of reporting standards for ML algorithms and performance metrics.

Main Results:

• A significant majority of studies (83%) neglect essential data preprocessing steps, impacting model accuracy.
• While model optimization is common (65%), model interpretability remains low (15%).
• Comparative evaluation of algorithms and appropriate metric selection are often insufficient for assessing model reliability.

Conclusions:

• There is a critical need to improve methodological rigor, particularly in data preprocessing and model interpretability, in ML groundwater research.
• Interdisciplinary collaboration and continuous innovation are necessary to fully leverage ML for sustainable groundwater management.
• This review offers a framework to guide the development of robust ML applications in groundwater quality assessment.