AI-Integrated Framework for Designing Optimized Groundwater Level Observation Networks Based on Hybrid Machine Learning and Stochastic Simulation Frameworks

International audience This study develops an integrated framework combining groundwater numerical modeling, probabilistic simulation, and machine learning to optimize the spatial design of an Optimized Groundwater Level Observation Network (OGLON) in the Mareth basin. A total of 565 existing monitoring wells were used to calibrate the groundwater flow model, complemented by stochastic groundwater simulations to train two AI-based approaches: the AI-Assisted Centroid Clustering (AIACC) algorithm and the Data-Driven Sparse Bayesian Learning (DDSBL) model. Three OGLON configurations were generated, AIACC (30 wells), DDSBL (30 wells), and Refined-DDSBL (30 wells), and benchmarked against the current monitoring network. Model performance indicates that the AIACC configuration reduces model error from 17,232 to 31.30, achieving an RMSE of 0.2145 m, significantly outperforming both the existing network (RMSE 0.5028 m) and the original DDSBL system (RMSE 0.6678 m). The Refined-DDSBL configuration provides the best overall accuracy, reducing model error from 21,355 to 1.32 and achieving the lowest RMSE (0.0153 m) and MAE (0.0091 m). Groundwater levels simulated under the proposed networks range between 3.8 m and 94.7 m, with the AIACC and Refined-DDSBL approaches offering improved spatial representation of key hydrogeological patterns compared to existing wells. Overall, results demonstrate a clear trade-off between computational efficiency (AIACC) and maximum predictive accuracy (Refined-DDSBL). Both AIACC and Refined-DDSBL significantly enhance spatial coverage and groundwater representation, confirming the effectiveness of integrating machine learning with groundwater modeling for cost-efficient and high-performance OGLON design.