Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
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 opera...
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| Veröffentlicht in: | Water resources management 2018-08, Vol.32 (10), p.3373-3389 |
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| creator | Allawi, Mohammed Falah Jaafar, Othman Ehteram, Mohammad Mohamad Hamzah, Firdaus El-Shafie, Ahmed |
| description | 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. |
| doi_str_mv | 10.1007/s11269-018-1996-3 |
| format | Article |
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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.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-018-1996-3</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Artificial intelligence ; Atmospheric Sciences ; Civil Engineering ; Computer simulation ; Earth and Environmental Science ; Earth Sciences ; Environment ; Evolutionary algorithms ; Genetic algorithms ; Geotechnical Engineering & Applied Earth Sciences ; Handling ; Hydrogeology ; Hydrologic models ; Hydrology ; Hydrology/Water Resources ; Irrigation water ; Learning algorithms ; Machine learning ; Marine fishes ; Mathematical models ; Nonlinear systems ; Nonlinearity ; Particle swarm optimization ; Policies ; Reservoir operation ; Software reliability ; Statistical analysis ; Swarm intelligence ; Synchronism ; Vulnerability ; Water demand</subject><ispartof>Water resources management, 2018-08, Vol.32 (10), p.3373-3389</ispartof><rights>Springer Science+Business Media B.V., part of Springer Nature 2018</rights><rights>Water Resources Management is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-b0e09d8956653a4d01da5e6c708496ba75a18bed982b27f5206afbafe6a0adf23</citedby><cites>FETCH-LOGICAL-c316t-b0e09d8956653a4d01da5e6c708496ba75a18bed982b27f5206afbafe6a0adf23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-018-1996-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-018-1996-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Allawi, Mohammed Falah</creatorcontrib><creatorcontrib>Jaafar, Othman</creatorcontrib><creatorcontrib>Ehteram, Mohammad</creatorcontrib><creatorcontrib>Mohamad Hamzah, Firdaus</creatorcontrib><creatorcontrib>El-Shafie, Ahmed</creatorcontrib><title>Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Atmospheric Sciences</subject><subject>Civil Engineering</subject><subject>Computer simulation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environment</subject><subject>Evolutionary algorithms</subject><subject>Genetic algorithms</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Handling</subject><subject>Hydrogeology</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Hydrology/Water Resources</subject><subject>Irrigation water</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Marine fishes</subject><subject>Mathematical models</subject><subject>Nonlinear systems</subject><subject>Nonlinearity</subject><subject>Particle swarm optimization</subject><subject>Policies</subject><subject>Reservoir operation</subject><subject>Software reliability</subject><subject>Statistical analysis</subject><subject>Swarm intelligence</subject><subject>Synchronism</subject><subject>Vulnerability</subject><subject>Water 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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.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-018-1996-3</doi><tpages>17</tpages></addata></record> |
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| subjects | Algorithms Artificial intelligence Atmospheric Sciences Civil Engineering Computer simulation Earth and Environmental Science Earth Sciences Environment Evolutionary algorithms Genetic algorithms Geotechnical Engineering & Applied Earth Sciences Handling Hydrogeology Hydrologic models Hydrology Hydrology/Water Resources Irrigation water Learning algorithms Machine learning Marine fishes Mathematical models Nonlinear systems Nonlinearity Particle swarm optimization Policies Reservoir operation Software reliability Statistical analysis Swarm intelligence Synchronism Vulnerability Water demand |
| title | Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
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