Chong, J. Y. and Hooi, G. L. and Goh, Q. Y. and Lai, V. and Huang, Y. F. and Koo, C. H. and El-Shafie, Ahmed and Ahmed, Ali Najah (2024) Adapting reservoir operations for optimal water management under varying climate and demand scenarios using metaheuristic algorithms. Ain Shams Engineering Journal, 15 (8). p. 102835. ISSN 2090-4479, DOI https://doi.org/10.1016/j.asej.2024.102835.
Full text not available from this repository.Abstract
The operational rule curve is significant for proper reservoir operations and water resources management of the dam. The optimisation of the release policy is rather a complicated and stochastic problem. Various types of algorithms have been used to develop the release policy for the Klang Gate Dam (KGD), but alas, climate parameters associated with climate change, be it regional or globally, e.g., temperature factors, had not been considered then. In this study, the consideration of maximum and minimum temperature factors is given emphasis, while water demand is evaluated in the optimisation problem using simulation-optimisation approaches. The support vector regression simulation model (SVRS), and the multilayer perceptron simulation model (MLPS) were used to simulate the demand for the scenario cases. SVRS utilises the hyperplane theory to study the relationship between the input and output data, whereas MLPS employs the human neural system to weight and process the inputs via multiple layers of neurons into a targeted output. Four distinct types of simulation-optimisation combination model, including the SVRS-firefly algorithm (SVRS-FA), the SVRS-particle swarm optimisation (SVRS-PSO), the MLPS-firefly algorithm (MLPS-FA), and the MLPS-particle swarm optimisation (MLPS-PSO), were applied and investigated. The ultimate goal of this study was to minimise the water deficit. The PSO optimisation models overall have outperformed the FA optimisation models in all aspects. The periodic reliability of the MLPS-PSO was found to be the highest when the minimum temperature scenario was considered, and is 0.6491, with the least shortage period of 80 months. The MLPS-PSO is also the most resilient model in the same scenario, with a resilience value of 0.6750. Whereas the SVRS-PSO was the least vulnerable model in the minimum temperature scenario with the lowest shortage index of 0.0047 and a volumetric deficiency of about 2.44 MCM in total. The SVRS-PSO model showed excellent performance for high and low inflow conditions, while the MLPS-PSO model was better during medium inflows. In short, each of the models investigated has its particular field of advancement, but since the maximum temperatures sounded critical, additional research for the maximum temperature case could be pursued further in depth.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Tunku Abdul Rahman (UTAR) , Malaysia, via the Universiti Tunku Abdul Rahman Research Fund (UTARRF) (IPSR/RMC/UTARRF/2020-C1/H01) |
| Uncontrolled Keywords: | Operational rule curve; Machine language model; Optimisation algorithms; Climate change parameters; Klang Gate Dam |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 26 Mar 2025 08:33 |
| Last Modified: | 26 Mar 2025 08:33 |
| URI: | http://eprints.um.edu.my/id/eprint/46800 |
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