Utilizing deep learning framework informed by physical principles for reservoir operations in Spain

Abstract

Reservoir operation plays a critical role in water resource management, especially in regions like Spain facing complex hydrological variability. This paper proposes an innovative approach that integrates deep learning techniques with established physical mechanisms to optimize reservoir operation strategies. The study focuses on the unique hydrological challenges faced by reservoirs in Spain and investigates the potential of employing a hybrid model to enhance reservoir management decisions. By merging deep learning algorithms with the principles of reservoir physics, this research aims to develop a robust framework capable of capturing complex hydrological dynamics while ensuring sustainable water resource utilization.