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Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mohammed Falah Allawi & Othman Jaafar & Mohammad Ehteram & Firdaus Mohamad Hamzah & Ahmed El-Shafie, 2018. "Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3373-3389, August.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:10:d:10.1007_s11269-018-1996-3
    DOI: 10.1007/s11269-018-1996-3
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    References listed on IDEAS

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    1. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
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    Cited by:

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    2. Zaher Mundher Yaseen & Mohammad Ehteram & Md. Shabbir Hossain & Chow Ming Fai & Suhana Binti Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Jaafar & Haitham Abdulmohsin Afan & Lai Sai Hin & Nuratiah, 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems," Sustainability, MDPI, vol. 11(7), pages 1-28, April.
    3. Mohammad Ehteram & Ali Najah Ahmed & Ming Fai Chow & Sarmad Dashti Latif & Kwok-wing Chau & Kai Lun Chong & Ahmed El-Shafie, 2023. "Optimal operation of hydropower reservoirs under climate change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10627-10659, October.

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