Machine Learning and Deep Learning Applications in Groundwater Quality Prediction: A Comprehensive Survey

Groundwater quality is critical for drinking, irrigation, and ecosystem health. Recent studies have leveraged machine learning (ML) and deep learning (DL) to model and predict groundwater quality parameters and indices more accurately and efficiently than traditional methods. This survey reviews significant research from the past few years on applying ML/DL techniques to groundwater quality analysis. We summarize each study's objectives, study area and data, modeling techniques, key input variables, performance metrics, and findings. Common approaches include predicting water quality indices (WQI) or specific contaminants algorithms including ensemble approaches, decision trees, artificial neural networks (ANN), support vector machines (SVM), and recurrent neural networks. According to the survey, ML/DL models offer insights into the most important water quality indicators and typically achieve excellent forecast accuracy for groundwater quality. A comparative summary of studies is presented to highlight methodological differences and performance outcomes. We wrap up with insights into prevailing challenges, such as data heterogeneity and model generalizability, and outlines future research directions.