Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

Mohammed Falah Allawi (), Othman Jaafar, Mohammad Ehteram, Firdaus Mohamad Hamzah and Ahmed El-Shafie
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Mohammed Falah Allawi: Universiti Kebangsaan Malaysia
Othman Jaafar: Universiti Kebangsaan Malaysia
Mohammad Ehteram: Semnan University
Firdaus Mohamad Hamzah: Universiti Kebangsaan Malaysia
Ahmed El-Shafie: University of Malaya

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 10, No 8, 3373-3389

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

Keywords: Reservoir operation; Shark machine learning algorithm; Artificial intelligent (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-018-1996-3

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