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Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model

Author

Listed:
  • Sabah Saadi Fayaed

    (Civil Engineering Department, Faculty of Engineering, Komar University of Science and Technology, Sulaymaniyah 00964, Iraq)

  • Seef Saadi Fiyadh

    (Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Wong Jee Khai

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia)

  • Ali Najah Ahmed

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
    Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, Selangor 43000, Malaysia)

  • Haitham Abdulmohsin Afan

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Rusul Khaleel Ibrahim

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Chow Ming Fai

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia)

  • Suhana Koting

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Nuruol Syuhadaa Mohd

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Wan Zurina Binti Jaafar

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Lai Sai Hin

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Ahmed El-Shafie

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

The simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively.

Suggested Citation

  • Sabah Saadi Fayaed & Seef Saadi Fiyadh & Wong Jee Khai & Ali Najah Ahmed & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Chow Ming Fai & Suhana Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Ja, 2019. "Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5367-:d:271654
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    References listed on IDEAS

    as
    1. V. Jothiprakash & Ganesan Shanthi, 2006. "Single Reservoir Operating Policies Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 917-929, December.
    2. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
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    Cited by:

    1. Sarmad Dashti Latif & Suzlyana Marhain & Md Shabbir Hossain & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2021. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    2. Dipsikha Devi & Anupal Baruah & Arup Kumar Sarma, 2022. "Characterization of dam-impacted flood hydrograph and its degree of severity as a potential hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 1989-2011, July.
    3. Suwapat Kosasaeng & Nirat Yamoat & Seyed Mohammad Ashrafi & Anongrit Kangrang, 2022. "Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization," Sustainability, MDPI, vol. 14(23), pages 1-14, December.

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