RESUMO
The study investigated the spatiotemporal relationship between surface hydrological variables and groundwater quality/quantity using geostatistical and AI tools. AI models were developed to estimate groundwater quality from ground-based measurements and remote sensing images, reducing reliance on laboratory testing. Different Kriging techniques were employed to map ground-based measurements and fill data gaps. The methodology was applied to analyze the Maragheh aquifer in northwest Iran, revealing declining groundwater quality due to industrial. discharges and over-extraction. Spatiotemporal analysis indicated a relationship between groundwater depth/quality, precipitation, and temperature. The Root Mean Square Scaled Error (RMSSE) values for all variables ranged from 0.8508 to 1.1688, indicating acceptable performance of the semivariogram models in predicting the variables. Three AI models, namely Feed-Forward Neural Networks (FFNNs), Support Vector Regression (SVR), and Adaptive Neural Fuzzy Inference System (ANFIS), predicted groundwater quality for wet (June) and dry (October) months using input variables such as groundwater depth, temperature, precipitation, Normalized Difference Vegetation Index (NDVI), and Digital Elevation Model (DEM), with Groundwater Quality Index (GWQI) as the target variable. Ensemble methods were employed to combine the outputs of these models, enhancing performance. Results showed strong predictive capabilities, with coefficient of determination values of 0.88 and 0.84 for wet and dry seasons. Ensemble models improved performance by up to 6% and 12% for wet and dry seasons, respectively, potentially advancing groundwater quality modeling in the future.