Operating a reservoir system based on the shark machine learning algorithm
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 cu...
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| Format: | Article |
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Springer Verlag (Germany)
2018
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| author | Allawi, Mohammed Falah Jaafar, Othman Mohamad Hamzah, Firdaus Ehteram, Mohammad Hossain, Md Shabbir El-Shafie, Ahmed |
| author_facet | Allawi, Mohammed Falah Jaafar, Othman Mohamad Hamzah, Firdaus Ehteram, Mohammad Hossain, Md Shabbir El-Shafie, Ahmed |
| author_sort | Allawi, Mohammed Falah |
| collection | UM |
| description | 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). |
| first_indexed | 2024-03-06T05:57:16Z |
| format | Article |
| id | um.eprints-22538 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:57:16Z |
| publishDate | 2018 |
| publisher | Springer Verlag (Germany) |
| record_format | dspace |
| spelling | um.eprints-225382020-02-28T01:41:56Z http://eprints.um.edu.my/22538/ 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 TA Engineering (General). Civil engineering (General) 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). Springer Verlag (Germany) 2018 Article PeerReviewed Allawi, Mohammed Falah and Jaafar, Othman and Mohamad Hamzah, Firdaus and Ehteram, Mohammad and Hossain, Md Shabbir and El-Shafie, Ahmed (2018) Operating a reservoir system based on the shark machine learning algorithm. Environmental Earth Sciences, 77 (10). p. 366. ISSN 1866-6280, DOI https://doi.org/10.1007/s12665-018-7546-8 <https://doi.org/10.1007/s12665-018-7546-8>. https://doi.org/10.1007/s12665-018-7546-8 doi:10.1007/s12665-018-7546-8 |
| spellingShingle | TA Engineering (General). Civil engineering (General) 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 |
| title | 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_short | Operating a reservoir system based on the shark machine learning algorithm |
| title_sort | operating a reservoir system based on the shark machine learning algorithm |
| topic | TA Engineering (General). Civil engineering (General) |
| work_keys_str_mv | AT allawimohammedfalah operatingareservoirsystembasedonthesharkmachinelearningalgorithm AT jaafarothman operatingareservoirsystembasedonthesharkmachinelearningalgorithm AT mohamadhamzahfirdaus operatingareservoirsystembasedonthesharkmachinelearningalgorithm AT ehterammohammad operatingareservoirsystembasedonthesharkmachinelearningalgorithm AT hossainmdshabbir operatingareservoirsystembasedonthesharkmachinelearningalgorithm AT elshafieahmed operatingareservoirsystembasedonthesharkmachinelearningalgorithm |