Operating a reservoir system based on the shark machine learning algorithm
Artificial intelligence; Decision making; Genetic algorithms; Learning systems; Reliability analysis; Reservoirs (water); Stochastic systems; Comprehensive analysis; High dams; Mathematical optimization model; Performance indicators; Semi-arid region; Stochastic features; Water deficits; Water relea...
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2023
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my.uniten.dspace-238292023-05-29T14:52:13Z Operating a reservoir system based on the shark machine learning algorithm Allawi M.F. Jaafar O. Mohamad Hamzah F. Ehteram M. Hossain M.S. El-Shafie A. 57057678400 6504503295 56266163500 57113510800 55579596900 16068189400 Artificial intelligence; Decision making; Genetic algorithms; Learning systems; Reliability analysis; Reservoirs (water); Stochastic systems; Comprehensive analysis; High dams; Mathematical optimization model; Performance indicators; Semi-arid region; Stochastic features; Water deficits; Water release; Learning algorithms; algorithm; machine learning; model; numerical model; optimization; performance assessment; reservoir; semiarid region; stochasticity; water demand; water stress; Aswan Dam; Aswan [Egypt]; Egypt; Chondrichthyes 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). � 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T06:52:13Z 2023-05-29T06:52:13Z 2018 Article 10.1007/s12665-018-7546-8 2-s2.0-85046870288 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046870288&doi=10.1007%2fs12665-018-7546-8&partnerID=40&md5=d641dac21d634c973ff01bed0e41f684 https://irepository.uniten.edu.my/handle/123456789/23829 77 10 366 Springer Verlag Scopus |
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Artificial intelligence; Decision making; Genetic algorithms; Learning systems; Reliability analysis; Reservoirs (water); Stochastic systems; Comprehensive analysis; High dams; Mathematical optimization model; Performance indicators; Semi-arid region; Stochastic features; Water deficits; Water release; Learning algorithms; algorithm; machine learning; model; numerical model; optimization; performance assessment; reservoir; semiarid region; stochasticity; water demand; water stress; Aswan Dam; Aswan [Egypt]; Egypt; Chondrichthyes |
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57057678400 Allawi M.F. Jaafar O. Mohamad Hamzah F. Ehteram M. Hossain M.S. El-Shafie A. |
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Article |
| author |
Allawi M.F. Jaafar O. Mohamad Hamzah F. Ehteram M. Hossain M.S. El-Shafie A. |
| spellingShingle |
Allawi M.F. Jaafar O. Mohamad Hamzah F. Ehteram M. Hossain M.S. El-Shafie A. Operating a reservoir system based on the shark machine learning algorithm |
| author_sort |
Allawi M.F. |
| title |
Operating a reservoir system based on the shark machine learning algorithm |
| title_short |
Operating a reservoir system based on the shark machine learning algorithm |
| title_full |
Operating a reservoir system based on the shark machine learning algorithm |
| title_fullStr |
Operating a reservoir system based on the shark machine learning algorithm |
| title_full_unstemmed |
Operating a reservoir system based on the shark machine learning algorithm |
| title_sort |
operating a reservoir system based on the shark machine learning algorithm |
| publisher |
Springer Verlag |
| publishDate |
2023 |
| _version_ |
1806428498865160192 |