Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios
Optimizing reservoir operation is a complex problem with non-linearities, numerous decision variables, and challenging constraints to simulate and solve. Researchers have tested various metaheuristics algorithms (MHAs) to reduce water deficit in reservoirs and presented them to decision-makers for a...
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my.uniten.dspace-366992025-03-03T15:44:00Z Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios Almubaidin M.A.A. Ahmed A.N. Malek M.A. Mahmoud M.A. Sherif M. El-Shafie A. 57476845900 57214837520 55636320055 55247787300 7005414714 16068189400 Jordan Errors Mean square error Optimization Reliability analysis Reservoir management Risk analysis Risk assessment Risk perception Sediments Water resources Charged system search Charged system search algorithms Charged system searches Reservoir operation Reservoir operation optimizations Sediment accumulation Sediment simulation Water deficit in reservoir Water deficits Water resources management algorithm error analysis geoaccumulation heuristics optimization sedimentation water demand water management water stress Reservoirs (water) Optimizing reservoir operation is a complex problem with non-linearities, numerous decision variables, and challenging constraints to simulate and solve. Researchers have tested various metaheuristics algorithms (MHAs) to reduce water deficit in reservoirs and presented them to decision-makers for adoption. Optimization methods vary depending on objectives, reservoir type, and algorithms used. The paper utilizes the CSS algorithm to study the impact of various scenarios on the optimal operation of the Mujib reservoir in Jordan to reduce water deficits using historical date between 2004 and 2019. The study explores different scenarios, including sediment impact, water demand management, and increasing the storage volume for the reservoir, to identify the optimal operation of the reservoir. The study compares the results of these scenarios with the current operation of the reservoir. Risk analysis (volumetric reliability, shortage index (SI), resilience, vulnerability) and error indexes (correlation coefficient R2, the root mean square error (RMSE), and the mean absolute error (MAE)) were used to compare results between scenarios, in addition to the annual water deficit values from the CSS algorithm for each scenario. The simulation of monthly sediment values in the Mujib reservoir showed that sediment accumulation accounts for 14.6% of the reservoir's volume at the end of 2019. Removing sediments retained by the dam can reduce water deficit by 19.42% when using the CSS algorithm. Additionally, reducing agricultural water demand by 11% and removing sediment reduced water deficit by 42.40%. The study also examined the impact of increasing the storage capacity of the reservoir by 10%, 20%, and 30%, revealing a decrease in water deficit by 35.44% when sediment removal was included in the analysis. The study examined the scenario of increasing the storage capacity of the Mujib reservoir by 30%, reducing water demand by 11%, and removing sediment. This scenario resulted in a 53.59% decrease in water deficit, providing decision-makers with viable solutions to address the water deficit problem in the reservoir. ? 2024 The Authors Final 2025-03-03T07:44:00Z 2025-03-03T07:44:00Z 2024 Article 10.1016/j.agwat.2024.108698 2-s2.0-85184080440 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184080440&doi=10.1016%2fj.agwat.2024.108698&partnerID=40&md5=291282c24c0dea68dc142da852a7bd59 https://irepository.uniten.edu.my/handle/123456789/36699 293 108698 All Open Access; Green Open Access; Hybrid Gold Open Access Elsevier B.V. Scopus |
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Jordan Errors Mean square error Optimization Reliability analysis Reservoir management Risk analysis Risk assessment Risk perception Sediments Water resources Charged system search Charged system search algorithms Charged system searches Reservoir operation Reservoir operation optimizations Sediment accumulation Sediment simulation Water deficit in reservoir Water deficits Water resources management algorithm error analysis geoaccumulation heuristics optimization sedimentation water demand water management water stress Reservoirs (water) |
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Jordan Errors Mean square error Optimization Reliability analysis Reservoir management Risk analysis Risk assessment Risk perception Sediments Water resources Charged system search Charged system search algorithms Charged system searches Reservoir operation Reservoir operation optimizations Sediment accumulation Sediment simulation Water deficit in reservoir Water deficits Water resources management algorithm error analysis geoaccumulation heuristics optimization sedimentation water demand water management water stress Reservoirs (water) Almubaidin M.A.A. Ahmed A.N. Malek M.A. Mahmoud M.A. Sherif M. El-Shafie A. Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios |
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Optimizing reservoir operation is a complex problem with non-linearities, numerous decision variables, and challenging constraints to simulate and solve. Researchers have tested various metaheuristics algorithms (MHAs) to reduce water deficit in reservoirs and presented them to decision-makers for adoption. Optimization methods vary depending on objectives, reservoir type, and algorithms used. The paper utilizes the CSS algorithm to study the impact of various scenarios on the optimal operation of the Mujib reservoir in Jordan to reduce water deficits using historical date between 2004 and 2019. The study explores different scenarios, including sediment impact, water demand management, and increasing the storage volume for the reservoir, to identify the optimal operation of the reservoir. The study compares the results of these scenarios with the current operation of the reservoir. Risk analysis (volumetric reliability, shortage index (SI), resilience, vulnerability) and error indexes (correlation coefficient R2, the root mean square error (RMSE), and the mean absolute error (MAE)) were used to compare results between scenarios, in addition to the annual water deficit values from the CSS algorithm for each scenario. The simulation of monthly sediment values in the Mujib reservoir showed that sediment accumulation accounts for 14.6% of the reservoir's volume at the end of 2019. Removing sediments retained by the dam can reduce water deficit by 19.42% when using the CSS algorithm. Additionally, reducing agricultural water demand by 11% and removing sediment reduced water deficit by 42.40%. The study also examined the impact of increasing the storage capacity of the reservoir by 10%, 20%, and 30%, revealing a decrease in water deficit by 35.44% when sediment removal was included in the analysis. The study examined the scenario of increasing the storage capacity of the Mujib reservoir by 30%, reducing water demand by 11%, and removing sediment. This scenario resulted in a 53.59% decrease in water deficit, providing decision-makers with viable solutions to address the water deficit problem in the reservoir. ? 2024 The Authors |
| author2 |
57476845900 |
| author_facet |
57476845900 Almubaidin M.A.A. Ahmed A.N. Malek M.A. Mahmoud M.A. Sherif M. El-Shafie A. |
| format |
Article |
| author |
Almubaidin M.A.A. Ahmed A.N. Malek M.A. Mahmoud M.A. Sherif M. El-Shafie A. |
| author_sort |
Almubaidin M.A.A. |
| title |
Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios |
| title_short |
Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios |
| title_full |
Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios |
| title_fullStr |
Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios |
| title_full_unstemmed |
Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios |
| title_sort |
enhancing reservoir operations with charged system search (css) algorithm: accounting for sediment accumulation and multiple scenarios |
| publisher |
Elsevier B.V. |
| publishDate |
2025 |
| _version_ |
1825816115566084096 |
| score |
13.244413 |