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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 胡明哲 | zh_TW |
| dc.contributor.advisor | Ming-Che Hu | en |
| dc.contributor.author | 侯昱安 | zh_TW |
| dc.contributor.author | Yu-An Hou | en |
| dc.date.accessioned | 2023-09-22T16:28:50Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-10 | - |
| dc.identifier.citation | 行政院農業委員會全球資訊網.(2016).氣候邊簽下農業灌溉水資源調適因應策略. Retrieved from https://www.coa.gov.tw/ws.php?id=2504446&RWD_mode=N&print=Y
經濟部水利署. (2022a). 公務統計報表. Retrieved from: https://www.wra.gov.tw/News_Content.aspx?n=2945&s=7396 經濟部水利署. (2022b). 各項用水統計資料庫. Retrieved from: https://www.wra.gov.tw/News_Content.aspx?n=2945&s=7396 經濟部水利署. (2023a). 水庫蓄水統計表. Retrieved from: https://fhy.wra.gov.tw/fhyv2/monitor/reservoir 經濟部水利署. (2023b). 現有公告水庫分布及營運概況. Retrieved from: https://www.wra.gov.tw/News_Content.aspx?n=2868&s=7002 經濟部水利署南區水資源局. (2018). 前瞻基礎建設水環境計畫曾文南化聯通管工程計畫. Retrieved from https://www-ws.wra.gov.tw/001/Upload/433/relfile/9992/10682/02ae86c3-0d73-43ef-8fcc-26877a9c1c10.pdf 經濟部水利署南區水資源局. (2022). 曾文南化聯通管工程計畫. Retrieved from file:///Users/andy/Downloads/%E6%9B%BE%E6%96%87%E5%8D%97%E5%8C%96%E8%81%AF%E9%80%9A%E7%AE%A1%E5%B7%A5%E7%A8%8B%E8%A8%88%E7%95%AB.pdf Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2011). Linear programming and network flows: John Wiley & Sons. Dreyfus, S. E., & Bellman, R. (1962). Applied dynamic programming: Princeton University Press. Efron, B. (1979). Computers and the theory of statistics: thinking the unthinkable. SIAM review, 21(4), 460-480. Fayaed, S. S., El-Shafie, A., & Jaafar, O. (2013). Reservoir-system simulation and optimization techniques. Stochastic Environmental Research and Risk Assessment, 27, 1751-1772. Fredrich, A. J. (1975). Hydrologic Engineering Methods for Water Resources Development. Volume 8. Reservoir Yield. Retrieved from Ghimire, B. N., & Reddy, M. J. (2014). Optimization and uncertainty analysis of operational policies for multipurpose reservoir system. Stochastic Environmental Research and Risk Assessment, 28, 1815-1833. Gleick, P. H. (1996). Basic water requirements for human activities: Meeting basic needs. Water international, 21(2), 83-92. Goodarzi, E., Ziaei, M., & Hosseinipour, E. Z. (2014). Introduction to optimization analysis in hydrosystem engineering: Springer. Hart, W. E., Laird, C. D., Watson, J.-P., Woodruff, D. L., Hackebeil, G. A., Nicholson, B. L., & Siirola, J. D. (2017). Pyomo-optimization modeling in python (Vol. 67): Springer. Hsu, S.-K. (1995). Shortage indices for water-resources planning in Taiwan. Journal of Water Resources Planning and Management, 121(2), 119-131. Koutsoyiannis, D. (2000). A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series. Water Resources Research, 36(6), 1519-1533. Loucks, D. P., & Van Beek, E. (2017). Water resource systems planning and management: An introduction to methods, models, and applications: Springer. Nakic, Z. (2018). Integrated Hydro-Economic Models as a Tool for the Sustainable Management of Water Resources. Journal of Environmental Geology. doi:10.4172/2591-7641.1000011 Osgood, B. (2002). Lecture notes for EE 261 the fourier transform and its applications. In. Rardin, R. L., & Rardin, R. L. (1998). Optimization in operations research (Vol. 166): Prentice Hall Upper Saddle River, NJ. Revelle, C. S., Loucks, D. P., & Lynn, W. R. (1968). Linear programming applied to water quality management. Water Resources Research, 4(1), 1-9. Shannon, C. E. (1949). Communication in the presence of noise. Proceedings of the IRE, 37(1), 10-21. SS, V. C., & Anand Hareendran, S. (2020). Optimal reservoir optimization using multiobjective genetic algorithm. Paper presented at the Advances in Swarm Intelligence: 11th International Conference, ICSI 2020, Belgrade, Serbia, July 14–20, 2020, Proceedings 11. Su, W.-R. (2000). 水資源供需指標建立之研究. National Central University, Voss, J. (2013). An introduction to statistical computing: a simulation-based approach: John Wiley & Sons. Whittaker, J. M. (1935). Interpolatory function theory. (No Title). Yasa, I. W., Bisri, M., Sholichin, M., & Andawayanti, U. (2018). Hydrological Drought Index Based on Reservoir Capacity – Case Study of Batujai Dam in Lombok Island, West Nusa Tenggara, Indonesia. Journal of Water and Land Development. doi:10.2478/jwld-2018-0052 Zhang, M., & Hu, C. (2016, 30 May-1 June 2016). Ultra High Frequency Polynomial and Cosine Artificial Higher Order Neural Networks. Paper presented at the 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89871 | - |
| dc.description.abstract | 在不同水庫之間分配水資源對政策制定者來說是一項困難的任務,因為他們必須確保水庫出水量能可以滿足各項用水。本文主要的目標是獲得最準確的多水庫水資源分配,並且探討曾文南化水庫聯通後的水資源分配狀況。因此需要一個水資源分配最佳化模型以提供參考。本文提出了一個考慮時間因素的網路流量最佳化模型,該演算法結合傅立葉頻譜分析,以解決水資源系統中的水資源分配問題。該方法應用於供應高雄與台南的四個水庫,曾文水庫、烏山頭水庫、南化水庫、高屏溪攔河堰。並且透過該模型分析未來曾文水庫與南化水庫聯通後的水資源分配狀況,並且與現行尚未聯通的水資源分配狀況進行比較。結果顯示,該演算法成功地有效最佳化水庫的出水量以滿足高雄台南地區對於這四個水庫的用水需求。並且利用缺水指標進行模型的評估,可以發現在聯通後各水庫各旬的缺水狀況都有所改善。 | zh_TW |
| dc.description.abstract | The allocation of water resources among different reservoirs poses a challenging task for policy makers, as they must ensure that the discharge from reservoirs can satisfy all water usage needs. The primary objective of this study is to obtain the most accurate multi-reservoir water allocation, and to explore the water resource allocation condition after the connection between the Zengwen and Nanhua reservoirs. Therefore, an optimization model for water resource allocation is required for reference. This paper proposes a network flow optimization model considering the temporal factor. This algorithm, combined with Fourier spectrum analysis, aims to solve the water resource allocation problem within the water resource system. This method is applied to four reservoirs that supply water to Kaohsiung and Tainan, namely the Zengwen Reservoir, Wushantou Reservoir, Nanhua Reservoir, and Gaoping River Weir. Moreover, through this model, we analyze the future water resource allocation situation after the connection between the Zengwen Reservoir and the Nanhua Reservoir, and compare it with the current situation where they are not yet connected. The results demonstrate that the algorithm effectively optimizes the outflow from the reservoirs to meet the water needs of the Kaohsiung and Tainan areas from these four reservoirs. Additionally, using a water shortage index to evaluate the model, it was found that the water shortage situation of each reservoir improved in every ten-day period after connection. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:28:50Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T16:28:50Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTACT iii CONTENT v LIST of FIGURES viii LIST of TABLES xi Chapter 1. Introduction 1 1.1 Backgorund 1 1.2 Motivation 6 1.3 Research Process 9 Chapter 2. Literature Review 10 2.1 Reservoir Optimization 10 2.2 Boostrapping Sampling Method 11 2.3 Water Shortage Index 13 Chapter 3. Methodology 16 3.1 Fourier Spectrum Analysis 16 3.1.1 Nyquist-Shannon sampling theorem 18 3.2 Network Flow Optimization Model 20 3.2.1 Minimal Cost Network Programming 20 3.2.2 Notations 26 3.2.3 One Node Case 28 3.2.4 Multiple Nodes Case 30 3.2.5 Proof of Enhancing Water Supply Capabilities through Reservoir Interconnection 34 3.2.6 Interior Point Method 37 Chapter 4. Study Area and Research Data 41 4.1 Comparison of the operation of each reservoir 43 4.1.1 Comparison of the annually water usage data of each reservoir 46 4.2 Research Data 48 Chapter 5. Result and Discussion 57 5.1 Objective and Methodology 57 5.2 Fourier Spectrum Analysis 58 5.2.1 Tsengwen reservoir fourier analysis result 59 5.2.2 Wushantou reservoir fourier analysis result 61 5.2.3 Nanhua rRservoir Fourier analysis result 63 5.2.4 Gaoping river weir fourier analysis result 65 5.2.5 Summary 66 5.3 Optimization Result 67 5.3.1 Scenario 1 70 5.3.2 Scenario 2 76 5.3.3 Summary 81 Chapter 6. Conclusions and Further Research 83 REFERENCE 85 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 氣候變遷 | zh_TW |
| dc.subject | 水庫水資源 | zh_TW |
| dc.subject | 網路流量最佳化模型 | zh_TW |
| dc.subject | 傅立葉頻譜分析 | zh_TW |
| dc.subject | 缺水指標 | zh_TW |
| dc.subject | Network Flow Optimization Model | en |
| dc.subject | Reservoir Water Resource | en |
| dc.subject | Climate Change | en |
| dc.subject | Water Shortage Index | en |
| dc.subject | Fourier Spectrum Analysis | en |
| dc.title | 水資源頻譜網路最佳化模型分析 | zh_TW |
| dc.title | Water Resources Spectrum Network Optimization Model | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 余化龍;許少瑜;游晟暐;陳沛芫 | zh_TW |
| dc.contributor.oralexamcommittee | Hwa-Lung Yu;Shao-Yiu Hsu;Cheng-Wei Yu;Pei-Yuan Chen | en |
| dc.subject.keyword | 水庫水資源,網路流量最佳化模型,傅立葉頻譜分析,缺水指標,氣候變遷, | zh_TW |
| dc.subject.keyword | Reservoir Water Resource,Network Flow Optimization Model,Fourier Spectrum Analysis,Water Shortage Index,Climate Change, | en |
| dc.relation.page | 87 | - |
| dc.identifier.doi | 10.6342/NTU202304051 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-12 | - |
| dc.contributor.author-college | 共同教育中心 | - |
| dc.contributor.author-dept | 統計碩士學位學程 | - |
| 顯示於系所單位: | 統計碩士學位學程 | |
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