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

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 Jaafar, Lai Sai Hin and Ahmed El-Shafie
Additional contact information
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
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

Sustainability, 2019, vol. 11, issue 19, 1-17

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.

Keywords: Sg. Langat Dam; reservoir operation policy; stochastic dynamic programming (SDP); optimization technique; simulation technique; artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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