Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

Mohammed Falah Allawi, Othman Jaafar, Mohammad Ehteram, Firdaus Mohamad Hamzah, Ahmed El-Shafie

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3373-3389
Number of pages17
JournalWater Resources Management
Volume32
Issue number10
DOIs
Publication statusPublished - 1 Aug 2018

Fingerprint

artificial intelligence
Dams
Artificial intelligence
dam
shark
Learning algorithms
Learning systems
water demand
Irrigation
genetic algorithm
Particle swarm optimization (PSO)
nonlinearity
Water
vulnerability
Genetic algorithms
irrigation
machine learning
simulation

Keywords

  • Artificial intelligent
  • Reservoir operation
  • Shark machine learning algorithm

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology

Cite this

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System. / Allawi, Mohammed Falah; Jaafar, Othman; Ehteram, Mohammad; Mohamad Hamzah, Firdaus; El-Shafie, Ahmed.

In: Water Resources Management, Vol. 32, No. 10, 01.08.2018, p. 3373-3389.

Research output: Contribution to journalArticle

Allawi, Mohammed Falah ; Jaafar, Othman ; Ehteram, Mohammad ; Mohamad Hamzah, Firdaus ; El-Shafie, Ahmed. / Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System. In: Water Resources Management. 2018 ; Vol. 32, No. 10. pp. 3373-3389.
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