Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
作者： Mohammed Falah Allawi , Othman Jaafar , Mohammad Ehteram , Firdaus Mohamad Hamzah , Ahmed El-Shafie
DOI: 10.1007/S11269-018-1996-3
关键词:
摘要: It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating system complex issue due to existing nonlinearity variables. Hence, determining modern model has high ability in handling operation crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) provide optimal operational rules. major objective for proposed minimizing deficit volume between water releases and irrigation demand. current compared performance of SML with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) Genetic (GA). models been utilized finding policies operate Timah Tasoh Dam, which located Malaysia. considerable statistical indicators explore efficiency models. simulation period showed SMLA approach outperforms both conventional algorithms. attained Reliability Resilience (Rel. = 0.98%, Res. 50%) minimum Vulnerability (Vul. 21.9 demand). demonstrated shark machine learning algorithm would be promising tool long-term optimization problem system.
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