Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis

Area of Science:

• Environmental Science
• Data Science
• Geology

Background:

• Groundwater pollution is a critical global issue impacting health and ecosystems.
• Machine learning (ML) offers advanced capabilities for analyzing complex groundwater data.
• Bibliometric analysis provides a systematic overview of research trends and contributions.

Purpose of the Study:

• To conduct a bibliometric analysis of machine learning applications in groundwater pollution research from 2000-2023.
• To identify key trends, leading contributors, and prevalent methodologies.
• To suggest future research directions for enhanced pollution control.

Main Methods:

• Bibliometric analysis of 1462 articles.
• Keyword frequency analysis to identify common machine learning techniques.
• Trend analysis of publication output and research focus evolution.

Main Results:

• Significant growth in research since 2020, led by China, USA, India, and Iran.
• Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Hierarchical Clustering Analysis (HCA) are dominant methods.
• Focus evolved from basic assessments to pollutant prediction, risk assessment, and source identification.

Conclusions:

• Machine learning is a vital tool for understanding and managing groundwater pollution.
• Addressing data quality, scarcity, and model interpretability are crucial for future progress.
• Prioritizing data sharing and theory-guided ML will advance effective remediation strategies.