Spatiotemporal Assessment and Machine Learning-Based Future Forecasting of Groundwater Hydro chemical Dynamics and Drawdown Variability

Pakistan's groundwater resources are vital to the country's water supply, yet increasingly threatened by issues such as over-extraction, inadequate management practices, and insufficient conservation regulations. This study was conducted to examine spatiotemporal aquifer behavior, fluctuations in drawdown levels, and water quality parameters like pH, Electrical Conductivity (EC), Total Dissolved Salts (TDS), Calcium, Magnesium, Total Hardness (TH), Bicarbonates and Chlorides by using geospatial techniques to address sustainable groundwater resource management needs. For future forecasting four machine learning (ML) models were used; Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF). Observed data were obtained from Water and Sanitation Agency (WASA) Faisalabad from year 2013 to 2023 which included 29 inline field area well stations and 25 Japan International Cooperation Agency (JICA) well stations, and weather data from the Terra Climate dataset. Groundwater drawdown patterns and quality changes over time were analyzed by GIS-based spatial analysis by utilizing historical data to train and test predictive models for 2024-2028. The XGBoost model demonstrated exceptional performance in predicting drawdown pre-monsoon (8.35m) and post-monsoon (7.65m) until 2028 and hydro chemical quality, with an average R-squared value of 0.86, RMSE below 0.08, and MAE under 0.05 for both. The study's spatial analysis revealed significant seasonal variations, with post-monsoon increases in mineral concentrations due to intensified leaching processes and identified a concerning rise in chloride levels after 2022, linked to anthropogenic activities. These findings underscored the importance of advanced machine learning techniques, particularly XGBoost, in accurately forecasting groundwater dynamics and hydro chemical quality.