Integrating MODFLOW and LSTM models for enhanced groundwater management in the coastal plains of Lattakia Governorate

ABSTRACT Effective groundwater management is vital for ensuring the sustainability of water resources, particularly in regions with significant agricultural and residential demands. This study focuses on the coastal plain of Lattakia Governorate, a 140-km2 area southeast of Lattakia city, bounded by the Mediterranean Sea, the Al-Kabir Al-Shamali River, and the Al-Sanobar River. The objective was to evaluate the performance of the MODFLOW model, within the Groundwater Modeling System (GMS), for simulating groundwater levels and to assess the predictive capabilities of Long Short-Term Memory (LSTM) networks for forecasting temporal changes in groundwater levels. The methodology involved data collection from thirty observation wells, preprocessing for model calibration, and the integration of MODFLOW and LSTM outputs to create a hybrid framework. Findings indicate that MODFLOW effectively simulates physical groundwater processes, while LSTM captures nonlinear and temporal dynamics. The integration of these models reduced prediction errors by up to 20% compared to standalone approaches, enhancing prediction accuracy and reliability. This study provides a novel approach to groundwater management, offering actionable insights for sustainable agricultural practices, residential water security, and pollution mitigation. The proposed framework demonstrates scalability and adaptability to similar hydrological settings globally, contributing to the advancement of integrated groundwater management strategies.