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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 張斐章 | |
dc.contributor.author | Chung-Lien Chen | en |
dc.contributor.author | 鄭仲廉 | zh_TW |
dc.date.accessioned | 2021-06-15T12:31:11Z | - |
dc.date.available | 2017-08-24 | |
dc.date.copyright | 2016-08-24 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-04 | |
dc.identifier.citation | 1. Chang, L. C., & Chang, F. J., 2009. Multi-objective evolutionary algorithm for operating parallel reservoir system. Journal of hydrology, 377(1), 12-20.
2. Chang, L. C., Chang, F. J., Wang, K. W., & Dai, S. Y., 2010. Constrained genetic algorithms for optimizing multi-use reservoir operation. Journal of Hydrology, 390(1), 66-74. 3. Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. , 2000. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Parallel problem solving from nature PPSN VI (pp. 849-858). Springer Berlin Heidelberg. 4. Forrester, J. W., & Forrester, J. W., 1969. Urban dynamics (Vol. 114). Cambridge: mIt press. 5. Ghumman, A. R., Khan, M. Z., Khan, A. H., & Munir, S., 2010. Assessment of operational strategies for logical and optimal use of irrigation water in a downstream control system. Irrigation and Drainage, 59(2), 117-128. 6. Kaini, P., Artita, K., & Nicklow, J. W., 2012. Optimizing structural best management practices using SWAT and genetic algorithm to improve water quality goals. Water resources management, 26(7), 1827-1845. 7. Mehta, B. K., & Goto, A., 1992. Design and operation of on-farm irrigation ponds. Journal of irrigation and drainage engineering, 118(5), 659-673. 8. Mehta, R., & Jain, S. K., 2009. Optimal operation of a multi-purpose reservoir using neuro-fuzzy technique. Water resources management, 23(3), 509-529. 9. Rani, D., & Moreira, M. M., 2010. Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water resources management, 24(6), 1107-1138. 10. Sterman, J. D., 1994. Learning in and about complex systems. System Dynamics Review, 10(2‐3), 291-330. 11. Vedula, S., & Kumar, D. N., 1996. An integrated model for optimal reservoir operation for irrigation of multiple crops. Water Resources Research, 32(4), 1101-1108. 12. Winz, I., Brierley, G., & Trowsdale, S., 2009. The use of system dynamics simulation in water resources management. Water resources management,23(7), 1301-1323. 13. Xi, X., & Poh, K. L., 2013. Using system dynamics for sustainable water resources management in Singapore. Procedia Computer Science, 16, 157-166. 14. 王國威, 2011. 智慧型區域水資源調配管理與休耕決策系統. 臺灣大學生物環境系統工程學研究所學位論文, 1-97. 15. 王昱中, 2014. 智慧型水資源調配策略以因應用水需求成長. 臺灣大學生物環境系統工程學研究所學位論文, 1-92. 16. 沈孟妍, 2012. 應用短期氣候預報於春耕乾旱休耕決策之探討-以大漢溪供水系統為例. 中央大學水文與海洋科學研究所學位論文, 1-183. 17. 吳俊宏, 2013. 支持向量機與遺傳演算法應用於水庫即時操作. 中原大學土木工程研究所學位論文, 1-112. 18. 吳阜峻, & 張良正., 2010. 通用型水資源調配模式之發展與應用-枯水期石門水庫缺水風險分析 (Doctoral dissertation). 19. 陳清田, 張煜權, & 洪振東., 2014. 灌溉管理操作對水稻產量與節水效能影響之研究. 農業工程學報, 60(1), 81-90. 20. 屠益民, 柯志昌, 吳濟華, & 張鴻斌., 2010. 動態系統導向之高雄市水污染治理之研究. 中山管理評論, 18(3), 863-895. 21. 曾雅彩, & 屠益民., 2007. 生產系統同步化的動態分析與設計. 中山管理評論,15(1), 95-116. 22. 楊舜年, 2015. 建立颱洪時期抽水站智慧型最佳化操作規則. 臺灣大學生物環境系統工程學研究所學位論文, 1-126. 23. 戴巧雯, 2014. 多目標遺傳演算法應用於滯洪池最佳化優選. 成功大學水利及海洋工程學系學位論文, 1-75. 24. 黃文政, & 周家慶., 2008. 桃園地區農業休耕時機之探討. 農業工程學報, 54(2), 21-34. 25. 許良瑋, 2011. 桃園埤塘輪灌系統之模擬分析. 中央大學土木工程學系學位論文, 1-91.桃園地區人工埤池對水資源輔助之分析研究. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50158 | - |
dc.description.abstract | 面對台灣水資源時空間分佈不均及人口膨脹、工商業發展蓬勃所導致的缺水問題,北部地區石門水庫用水調度將是ㄧ個重大課題。本研究透過灌溉用水的調度彈性支援非灌溉用水標的,建置因應未來都市化用水衝擊的整合型水資源智慧管理系統,期能有效率地使用水資源並降低乾旱缺水所帶來的供水壓力及經濟損失,此研究並考慮桃園地區埤塘系統備援以增加本研究水資源系統之供應承載力。
本研究蒐集2005-2014年桃園地區農業、工業以及人口發展等長期歷史資料,應用系統動力模式,投射未來2015-2030年桃園地區需水情勢,並參考1977、1984、2002年等乾旱年之石門水庫旬入流量及水庫初始有效庫容50%、40%、30%等情境,共設定九種未來供水可能狀況,運用M-5操作規則模擬未來缺水嚴重程度;並透過非支配排序遺傳演算法-II(NSGA-II)搜尋最大平均有效蓄水率(RRS)及最小修正缺水指標(MSI)等兩個目標;透過NSGA-II搜尋於各水文情境的表現,結果顯示修正缺水指標(MSI)有著6.9%~24%的改善率,各旬平均有效蓄水率(RRS)最高可達9.6%的改善率;若在考量埤塘備援系統的情況下,修正缺水指標(MSI)改善率最高更可達35.5%,各旬平均有效蓄水率(RRS)最高達1.9%。 根據研究結果顯示,透過NSGA-II搜尋的最佳供水操作策略以及埤塘備援系統的支援,可有效因應未來都市化需水情勢,作為決策者於水資源管理上的參考依據。 | zh_TW |
dc.description.abstract | Facing the uneven spatio-temporal distribution of water resources and the increasing water demands caused by population growth and industrial development, the water regulation of the Shimen Reservoir for Taoyuan has become a critical issue. This study aims to build an intelligent water allocation system in order to suitably make water regulation with flexible water transfer from irrigation sectors to industrial and municipal sectors for reducing water pressure in public sectors during drought periods. In addition, the farm ponds in Taoyuan are also considered as a back-up water resource in this study for enhancing the resilience of the intelligent water allocation system.
This study first simulates the future water demands of Taoyuan for the period of 2015 and 2030 by using the system dynamics theory based on agricultural and industrial data as well as population statistics collected during 2005 and 2014. We next design nine water supply scenarios in response to the possible drought conditions in the future based on the ten-day inflow data collected from the Shimen Reservoir in three drought years (1977, 1984, 2002) and three initial reservoir storage capacities (50%, 40%, 30%) of these drought years. According to the simulation results of future water demand and supply, the M-5 rule curves are used to simulate the water shortage conditions during 2015 and 2030 while the non-dominated sorting genetic algorithm-II (NSGA-II) is used to search the minimal modified shortage index (MSI) and the maximal ratio of effective reservoir storage capacity. The results of the NSGA-II for these nine designed scenarios indicate that the improvement rates of the MSI (as compared to the MSI obtained from M5 rule curves) ranges between 6.9% and 24% while the averaged effective reservoir storage capacity ratio for ten-day periods reaches as high as 9.6%. When the back-up water resource of farm ponds in the study area is incorporated into the proposed intelligent water allocation system, the new results of the NSGA-II indicate that the improvement rates of the MSI and the averaged effective reservoir storage capacity ratio for ten-day periods will further reach as high as 35.5% and 1.9%, respectively. The results of this study demonstrate that the multi-objective reservoir operation strategy obtained from the NSGA-II with the back-up water resource of farm ponds can make effective water allocation in response to urban water demands and thus provide decision makers with reference guidelines in sustainable water resources management. We hope that the proposed intelligent water allocation system will pave the way to future research for integrated water resources management. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T12:31:11Z (GMT). No. of bitstreams: 1 ntu-105-R03622032-1.pdf: 2149399 bytes, checksum: fcec831fe60a4987bd34ee7cc552fc40 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 摘要 i
Abstract v 第一章 緒論 1 1.1. 研究背景 1 1.2. 研究目的 2 1.3. 研究架構 3 第二章 文獻回顧 5 2.1. 系統動力學相關理論 5 2.2. 遺傳演算法相關應用文獻 6 2.3. 水庫操作相關文獻 6 2.4. 農業灌溉管理相關文獻 7 第三章 理論概述 9 3.1. 遺傳演算法(Genetic Algorithm,GA) 9 3.1.1. 遺傳演算法(GA)演算流程 9 3.2. 非支配排序遺傳演算法-II(NSGA-II) 11 3.3. 系統動力學 17 第四章 研究案例 21 4.1. 研究區域概況 21 4.1.1. 桃園地區工業發展概述 21 4.1.2. 桃園地區農業發展概述 25 4.1.3. 桃園地區人口發展概述 28 4.2. 桃園地區未來需水推估 29 4.2.1. 工業用水推估 29 4.2.2. 民生用水推估 30 4.2.3. 農業用水推估 34 4.3. 石門水庫概述 36 4.4. 石門水庫供水操作 38 4.4.1. 石門水庫操作規線 38 4.4.2. 石門水庫水資源運用系統 40 4.4.3. 石門水庫放水模擬M-5操作分析步驟 41 4.4.4. 石門水庫放水NSGA-II優化操作分析步驟 43 4.5. 水資源備援系統設定 47 4.5.1. 桃園地區埤塘現況 47 4.5.2. 模式埤塘備援模擬設定 50 4.5.3. 埤塘備援系統操作分析步驟 51 第五章 結果與討論 53 5.1. 民生用水既工業用水推估 53 5.1.1. 農業用水推估 56 5.2. 石門水庫放水優化操作模擬結果 57 5.3. 石門水庫放水優化結合埤塘備援模擬結果 65 第六章 結論與建議 72 6.1. 結論 72 6.2. 建議 74 參考文獻 75 附錄 78 | |
dc.language.iso | zh-TW | |
dc.title | 因應都市化影響之智慧型水資源管理系統 | zh_TW |
dc.title | Intelligent Water Management System
Under the Influence of Urbanization | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張麗秋,黃文政,陳永祥 | |
dc.subject.keyword | 水資源管理,都市化,非支配排序遺傳演算法-II(NSGA-II),系統動力模式,埤塘備援系統,多目標水庫操作, | zh_TW |
dc.subject.keyword | Water resources management,Urbanization,Non-dominated sorting genetic algorithm-II (NSGA-II),System dynamics,farm ponds,Multi-objective reservoir operation, | en |
dc.relation.page | 89 | |
dc.identifier.doi | 10.6342/NTU201601834 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-04 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
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