Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia

Developing efficient methods for groundwater level (GWL) prediction is essential for identifying the groundwater flow pattern, characterizing the spatial extent of contaminant plumes, and enhancing water resources management. Recently, significant advances have been made in predicting GWL using machine learning (ML) models, but these do not consider hydrogeological heterogeneities that condition the flow pattern. This study develops and evaluates the applicability of advanced geostatistics and ML models to characterize the spatial variability of the GWL, taking into account the discontinuities induced by complex geological environments and leveraging only piezometer positions and monitored GWL. Geostatistical-based ordinary kriging (G/OK) and kernel ridge regression (KRR) were conducted on joint-faulted coastal aquifer systems in southeastern Tunisia. Geological knowledge was incorporated into the characterization process, achieving better function modeling, and optimizing both geostatistical and ML models. The present work counts among the first ML applications that take into account the spatial variability modeling constrained by geological heterogeneities. The task is especially challenging as actual data points are scarce. The results are evaluated using cross-validation with several error and evaluation metrics. Comparative analyses were performed to assess the consistency with the hydrogeological reality. The proposed approaches generated credible GWL maps that reproduce the regional and local flow patterns. A comprehensive interpretation provides a range of essential insights on the spatial variation of the groundwater flow path and the hydraulic behavior of faults acting as conduits, barriers, or conduit-barriers. The implemented model could be applied to other analogous areas to assess GWL and other hydraulic parameters efficiently.