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https://ptsldigital.ukm.my/jspui/handle/123456789/486969Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ahmed El-Shafie,Prof. Dr. | - |
| dc.contributor.author | Md. Shabbir Hossain (P54849) | - |
| dc.date.accessioned | 2023-10-11T02:27:04Z | - |
| dc.date.available | 2023-10-11T02:27:04Z | - |
| dc.date.issued | 2013-02-08 | - |
| dc.identifier.other | ukmvital:120075 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/486969 | - |
| dc.description | Reservoir release policy is a highly sensitive issue in water resources management as it directly controls the water uses in different aspect of a nation. In calibrating a reservoir release policy, the conventional methods, like dynamic programming (DP), linear programming (LP) and nonlinear programming (NLP) may fail to produce satisfactory optimum results in solving them due to their own limitations. DP is not suitable for a large dimensional problem and need discrete variable spaces, LP required modifying the objective function in a linear form and NLP is not convenient to use for a problem consisting a huge number of constraints and variable bounds which is very obvious in case of reservoir operation optimization. In addition, other popular and well-established optimization algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) have the formation limitations and disadvantages too for using in this type of optimization problem. GA has the complexity of using evolutionary operators and premature convergences problems and PSO may suffer from entrapping in local optima. This study has introduced a newly developed search algorithm named as artificial bee colony (ABC) optimization in solving reservoir release optimization problems. Minimization of the water deficit for monthly operation has considered as the main objective of the study. While achieving the objective, the study has proposed ABC in reservoir release optimization research as it free from the difficulties of previously used optimization techniques. The proposed ABC optimization has been compared with two leading optimization techniques in this field - GA and PSO. The study has investigated the performances of ABC, PSO and GA optimization models on two case studies - Klang Gates dam (small) and Aswan High dam (large), categorized in terms of reservoir physical and operational characteristics. In calibrating the release policy, three inflow situations (high, medium and low) have constructed from the historical data analysis for both reservoir. Simulation has done by using actual historical inflow to perform the risk analysis for each model. Different performance measuring indices (such as reliability, resiliency, vulnerability etc.) has computed and compared for this purpose. The results showed that for both types of reservoir (large and small) ABC outperformed in terms of reliability, resiliency and vulnerability. The ABC driven release policy has handled the small as well as large and complex reservoir system smoothly where GA and PSO has found not to be able to reach that much satisfaction level for large and complex reservoir. In case of small and simple reservoir the reliability measures are 74 %, 71.5 %, 70% and 52% respectively for ABC, PSO, real coded GA and binary GA model. For large and complex reservoir the reliability measures obtained from ABC, PSO, real coded GA and binary GA models are 98.14 %, 97.69 %, 91.66 % and 70.8 %. ABC release policy also has the capability to save water from wastages as the simulated results showed lowest number of oversupply period,Certification of Master's/Doctoral Thesis" is not available | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | - |
| dc.rights | UKM | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.subject | Artificial intelligence | - |
| dc.subject | Reservoirs | - |
| dc.title | Adopting artificial intelligences in optimizing reservoir operation policy | - |
| dc.type | Theses | - |
| dc.format.pages | 149 | - |
| dc.identifier.callno | TD395.M837 | - |
| dc.identifier.barcode | 002743(2013) | - |
| Appears in Collections: | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_120075+SOURCE1+SOURCE1.0.PDF Restricted Access | 1.05 MB | Adobe PDF |  View/Open |
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