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Operating a reservoir system based on the shark machine learning algorithm

  • Mohammed Falah Allawi
  • Othman Jaafar
  • Firdaus Mohamad Hamzah
  • Mohammad Ehteram
  • Md. Shabbir Hossain
  • Ahmed El-Shafie
Original Article

Abstract

The operating process of a multi-purpose reservoir needs to develop models that have the ability to overcome the challenges facing the decision makers. Therefore, the development of a mathematical optimization model is crucial for selecting the optimal policies for the reservoir operation. In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. The SMLA began with a group of randomly produced potential solutions and later interactively executed the search for the optimal solution. The procedure for the SMLA is suitable to be applied to a reservoir system due to its ability to tackle the stochastic features of dam and reservoir systems. The major purpose of the proposed models is to generate an operation rule that could minimize the absolute value of the differences between water release and water demand. The proposed model has been examined using the data of the Aswan High Dam, Egypt as the case study. The performance of the SMLA was compared with the performance of the most widespread evolutionary algorithms, namely, the genetic algorithm (GA). Comprehensive analysis of the results was performed using three performance indicators, namely, resilience, reliability, and vulnerability. This work concluded that the performance of the SMLA model was better than the GA model in generating the optimal policy for reservoir operation. The result showed that the SMLA succeeded in providing high reliability (99.72%), significant resilience (1) and minimum vulnerability (20.7% of demand).

Keywords

Semi-arid region Water deficit Water release Aswan High Dam 

Notes

Acknowledgements

The research is funded by the “Bold Grant” of University Tenaga Nasional (10289176/B/9/2017/57) and University of Malaya Research Grant “UMRG” (RP025A-18SUS). The authors are grateful to the UNITEN and Institute of Energy Infrastructure and University of Malaya, Malaysia for supporting the study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mohammed Falah Allawi
    • 1
  • Othman Jaafar
    • 1
  • Firdaus Mohamad Hamzah
    • 1
  • Mohammad Ehteram
    • 2
  • Md. Shabbir Hossain
    • 3
  • Ahmed El-Shafie
    • 4
  1. 1.Civil and Structural Engineering Department, Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Water Engineering and Hydraulic Structures, Faculty of Civil EngineeringSemnan UniversitySemnanIran
  3. 3.Department of Civil EngineeringUniversiti Tenaga NasionalKajangMalaysia
  4. 4.Department of Civil Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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