A Hybrid Physically Based–AI Framework for Improving Groundwater Level Simulations

The combination of physically based models and artificial intelligence techniques enhances the simulation of piezometric levels by integrating the hydrological consistency of the former with the ability of the latter to capture nonlinear patterns and reduce uncertainties, ultimately providing more robust predictions. In this work, we coupled a Modflow groundwater flow model with nonlinear autoregressive neural networks with exogenous input (NARX) to simulate piezometric levels in the Campo de Montiel groundwater body (GWB).
The Campo de Montiel GWB, located in the Upper Guadiana Basin (south‑eastern Spain), represents a critical area where groundwater‑dependent ecosystems coexist in tension with intensive groundwater abstraction, mainly for irrigation. This aquifer plays a key role in the regional hydrological system and constitutes an essential water reservoir in this semi‑arid environment.
A numerical Modflow model developed by the river basin authority was used to simulate groundwater flow and river–aquifer interactions across the eight groundwater bodies of the Upper Guadiana Basin, providing hydraulic head maps and flow budgets. In a subsequent step, NARX neural networks were trained to reproduce piezometric levels using the Modflow‑simulated heads as exogenous inputs in Campo de Montiel groundwater body.
This hybrid modelling approach improved the accuracy of piezometric level simulations compared to the standalone flow model. For the pilot piezometer, the Modflow model yielded an RMSE of 8.12 m, whereas the hybrid approach reduced the RMSE to 5.08 m.

Funding: This research was partially funded by the project SIGLO-PRO (PID2021- 128021OB - I00/ AEI / https://doi.org/10.13039/501100011033/FEDER,UE), from the Spanish Ministry of Science, Innovation and Universities.