Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique
To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a comple...
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Nature Research
2024
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| author | Lai V. Huang Y.F. Koo C.H. Ahmed A.N. Sherif M. El-Shafie A. |
| author2 | 57204919704 |
| author_facet | 57204919704 Lai V. Huang Y.F. Koo C.H. Ahmed A.N. Sherif M. El-Shafie A. |
| author_sort | Lai V. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09. � 2023, The Author(s). |
| format | Article |
| id | my.uniten.dspace-33874 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | my.uniten.dspace-338742024-10-14T11:17:22Z Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique Lai V. Huang Y.F. Koo C.H. Ahmed A.N. Sherif M. El-Shafie A. 57204919704 55807263900 57204843657 57214837520 7005414714 16068189400 article bee drug efficacy flooding genetic algorithm hawk learning metaheuristics nonhuman particle swarm optimization reliability risk assessment vulnerability water deficit water loss water supply To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09. � 2023, The Author(s). Final 2024-10-14T03:17:22Z 2024-10-14T03:17:22Z 2023 Article 10.1038/s41598-023-33801-z 2-s2.0-85156228113 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156228113&doi=10.1038%2fs41598-023-33801-z&partnerID=40&md5=bde990902ba58b91eca26ed139da30e1 https://irepository.uniten.edu.my/handle/123456789/33874 13 1 6966 All Open Access Gold Open Access Green Open Access Nature Research Scopus |
| spellingShingle | article bee drug efficacy flooding genetic algorithm hawk learning metaheuristics nonhuman particle swarm optimization reliability risk assessment vulnerability water deficit water loss water supply Lai V. Huang Y.F. Koo C.H. Ahmed A.N. Sherif M. El-Shafie A. Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique |
| title | Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique |
| title_full | Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique |
| title_fullStr | Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique |
| title_full_unstemmed | Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique |
| title_short | Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique |
| title_sort | optimal water supply reservoir operation by leveraging the meta-heuristic harris hawks algorithms and opposite based learning technique |
| topic | article bee drug efficacy flooding genetic algorithm hawk learning metaheuristics nonhuman particle swarm optimization reliability risk assessment vulnerability water deficit water loss water supply |
| url_provider | http://dspace.uniten.edu.my/ |