Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled water production, and commercial use. In recent years, due to pressures from climate change and excessive urbanization, a noticeable decline in groundwater levels has been observed, particularly in arid and semi-arid regions. The corresponding changes have been analyzed using a diverse range of methodologies, including data-driven modeling techniques. Recent evidence has shown a notable acceleration in the utilization of such advanced techniques, demonstrating significant attention by the research community. Therefore, the major aim of the present study is to conduct a bibliometric analysis to investigate the application and evolution of machine learning (ML) techniques in groundwater research. In this sense, studies published between 2000 and 2023 were examined in terms of scientific productivity, collaboration networks, research themes, and methods. The findings revealed that ML techniques offer high accuracy and predictive capacity, especially in water quality, water level estimation, and pollution modeling. The United States, China, and Iran stand out as leading countries emphasizing the strategic importance of ML in groundwater management. However, the outcomes demonstrated that a low level of international cooperation has led to deficiencies in solving transboundary water problems. The study aimed to encourage more widespread and effective use of ML techniques in water management and environmental planning processes and drew attention to the importance of transparent and interpretable algorithms, with the potential to yield rewarding opportunities in increasing the adoption of corresponding technologies by decision-makers.