Emerging Soft Computing Technologies for Groundwater Quality Monitoring, Prediction and Management: A Comprehensive Review

Groundwater is a vital resource for domestic, agricultural and industrial activities worldwide. Rapid urbanization, industrialization, intensive agricultural practices and climate change have increased the risks of groundwater pollution and depletion. Conventional monitoring methods based on field sampling and laboratory analysis are often time-consuming, costly and limited in spatial and temporal coverage, restricting timely assessment and effective groundwater management. Recent advances in artificial intelligence (AI), machine learning (ML), geographic information systems (GIS), remote sensing (RS) and the Internet of Things (IoT) offer new opportunities to develop integrated monitoring networks to sustain long-term water security through efficient groundwater quality assessment, pollution prediction, contaminant tracking and sustainable water resource management. Following a PRISMA-based screening and eligibility assessment process, 103 relevant studies were selected and systematically analyzed to evaluate recent advances in AI, ML, GIS, RS and IoT applications for groundwater quality monitoring and management This review provides an updated overview of various recent studies and developments concerning the use of AI, ML, GIS, RS and IoT in groundwater monitoring, pollution mapping, health-risk evaluation and intelligent decision-making. It primarily emphasizes on applications of various machine learning algorithms namely artificial neural network (ANN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and deep learning in prediction of groundwater quality and water quality index (WQI). This review also focuses on aspects of spatial analysis using GIS and RS, vulnerability assessment and detection of pollution hot-spot and on-line groundwater quality monitoring through IoT-based networks. In addition, some main challenges such as data availability, interpretability of models, reliability of sensors, cyber security and technology implementation are discussed critically. Potential future applications such as explainable AI (XAI), Digital Twins, edge computing, federated learning and intelligent decision support system (IDSS) are also presented. The study concludes that inter-disciplinary integration of environmental science, geospatial technologies and artificial intelligence can bring about robust, scalable and intelligent groundwater.