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

Research output: Contribution to journalArticle

5 Citations (Scopus)

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).

Original languageEnglish
Article number366
JournalEnvironmental Earth Sciences
Volume77
Issue number10
DOIs
Publication statusPublished - 1 May 2018

Fingerprint

artificial intelligence
shark
sharks
Learning algorithms
Learning systems
Dams
dams (hydrology)
genetic algorithm
Genetic algorithms
vulnerability
dam
Water
Evolutionary algorithms
machine learning
water demand
Egypt
water
case studies

Keywords

  • Aswan High Dam
  • Semi-arid region
  • Water deficit
  • Water release

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • Water Science and Technology
  • Soil Science
  • Pollution
  • Geology
  • Earth-Surface Processes

Cite this

Operating a reservoir system based on the shark machine learning algorithm. / Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Ehteram, Mohammad; Hossain, Md Shabbir; El-Shafie, Ahmed.

In: Environmental Earth Sciences, Vol. 77, No. 10, 366, 01.05.2018.

Research output: Contribution to journalArticle

Allawi, Mohammed Falah ; Jaafar, Othman ; Mohamad Hamzah, Firdaus ; Ehteram, Mohammad ; Hossain, Md Shabbir ; El-Shafie, Ahmed. / Operating a reservoir system based on the shark machine learning algorithm. In: Environmental Earth Sciences. 2018 ; Vol. 77, No. 10.
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