International Journal of Hydropower and Civil Engineering

P-ISSN: 2707-8302, E-ISSN: 2707-8310
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2023, Vol. 4, Issue 1, Part A

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


Author(s): Jessica Medina and Alberto Ribal

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.

DOI: 10.22271/27078302.2023.v4.i1a.17

Pages: 07-09 | Views: 112 | Downloads: 41

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How to cite this article:
Jessica Medina, Alberto Ribal. Utilizing deep learning framework informed by physical principles for reservoir operations in Spain. Int J Hydropower Civ Eng 2023;4(1):07-09. DOI: 10.22271/27078302.2023.v4.i1a.17
International Journal of Hydropower and Civil Engineering

International Journal of Hydropower and Civil Engineering

International Journal of Hydropower and Civil Engineering
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