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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer Verlag
2018
|
| Subjects: |
| _version_ | 1825721891215638528 |
|---|---|
| author | Allawi, Mohammed Falah Jaafar, Othman Ehteram, Mohammad Mohamad Hamzah, Firdaus El-Shafie, Ahmed |
| author_facet | Allawi, Mohammed Falah Jaafar, Othman Ehteram, Mohammad Mohamad Hamzah, Firdaus El-Shafie, Ahmed |
| author_sort | Allawi, Mohammed Falah |
| collection | UM |
| 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. |
| first_indexed | 2024-03-06T05:57:16Z |
| format | Article |
| id | um.eprints-22539 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:57:16Z |
| publishDate | 2018 |
| publisher | Springer Verlag |
| record_format | dspace |
| spelling | um.eprints-225392019-09-25T04:26:11Z http://eprints.um.edu.my/22539/ Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System Allawi, Mohammed Falah Jaafar, Othman Ehteram, Mohammad Mohamad Hamzah, Firdaus El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) 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. Springer Verlag 2018 Article PeerReviewed Allawi, Mohammed Falah and Jaafar, Othman and Ehteram, Mohammad and Mohamad Hamzah, Firdaus and El-Shafie, Ahmed (2018) Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System. Water Resources Management, 32 (10). pp. 3373-3389. ISSN 0920-4741, DOI https://doi.org/10.1007/s11269-018-1996-3 <https://doi.org/10.1007/s11269-018-1996-3>. https://doi.org/10.1007/s11269-018-1996-3 doi:10.1007/s11269-018-1996-3 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Allawi, Mohammed Falah Jaafar, Othman Ehteram, Mohammad Mohamad Hamzah, Firdaus El-Shafie, Ahmed Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
| title | Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
| title_full | Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
| title_fullStr | Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
| title_full_unstemmed | Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
| title_short | Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System |
| title_sort | synchronizing artificial intelligence models for operating the dam and reservoir system |
| topic | TA Engineering (General). Civil engineering (General) |
| work_keys_str_mv | AT allawimohammedfalah synchronizingartificialintelligencemodelsforoperatingthedamandreservoirsystem AT jaafarothman synchronizingartificialintelligencemodelsforoperatingthedamandreservoirsystem AT ehterammohammad synchronizingartificialintelligencemodelsforoperatingthedamandreservoirsystem AT mohamadhamzahfirdaus synchronizingartificialintelligencemodelsforoperatingthedamandreservoirsystem AT elshafieahmed synchronizingartificialintelligencemodelsforoperatingthedamandreservoirsystem |