Machine Learning-based Model for Groundwater Quality Prediction: A Comprehensive Review and Future Time–Cost Effective Modelling Vision

Towards a better groundwater management, developing a prediction model for groundwater quality is of utmost importance. The conventional method of measuring groundwater quality data often associated with errors due to the lengthy duration of investigation of the parameters as well as the tremendous effort and time involved in gathering and analysing the samples. The expense associated with determining the parameters’ values via laboratory testing is substantial. There has been a notable increase in machine learning (ML) application for modelling groundwater quality as of recent, evidenced by a wealth of studies reporting impressive results. This paper provides an extensive examination of 91 relevant articles picked from the Web of Science and PubMed, from 2015 to 2024. The focus of the review revolves on significant ML algorithms, including artificial neural networks (ANN), random forest (RF), support vector machines (SVM), hybrid models, and other algorithms that have demonstrated efficacy in predicting groundwater quality, such as k-nearest neighbours and extreme gradient boosting (XGBoost). Critical modelling concepts such as data splitting, utilized parameters, performance metrics, and study areas were addressed, emphasizing optimal practices for effective groundwater quality prediction with ML.