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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 | 林國峰(Gwo-Fong Lin) | |
dc.contributor.author | Chun-An Wang | en |
dc.contributor.author | 王浚安 | zh_TW |
dc.date.accessioned | 2023-03-19T22:04:42Z | - |
dc.date.copyright | 2022-09-06 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-07-18 | |
dc.identifier.citation | 1. Liao CY (2021) Long-term streamflow forecasting using an AI-based rainfall-runoff model with TCWB1T1 output. Master's theses, National Taiwan University 2. John H (1992) Genetic Algorithms. Scientific American 267:66–73 3. Sharif M, Wardlaw R (2000) Multireservoir Systems Optimization Using Genetic Algorithms: Case Study. Journal of Computing in Civil Engineering 14(4):255–263 4. Van Zyl J, Savic D, Walters G (2004) Operational Optimization of Water Distribution Systems Using a Hybrid Genetic Algorithm. Water Resources Planning and Management 130:160–170 5. Feng Z, Niu WJ, Cheng CT (2018) Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm. Energy 153:706–718 6. Tian X, Guo Y, Negenborn R, Wei L, Lin NM, Maestre JM (2019) Multi-Scenario Model Predictive Control Based on Genetic Algorithms for Level Regulation of Open Water Systems under Ensemble Forecasts. Water Resources Management 33:3025–3040 7. Kuo TL, Chang LC (2000) Optimizing the Rule Curves for Multi-Reservoir Operations Using Genetic Algorithm. Master's theses, National Yang Ming Chiao Tung University 8. Wang KW (2011) Intelligent regional water resources management and fallow decision-making system. Doctoral dissertation, National Taiwan University 9. Young G (1967) Finding Reservoir Operating Rules. Journal of the Hydraulics Division 93 (6):297–322 10. Becker L, Yeh W (1974) Optimization of real time operation of a multiple-reservoir system. Water Resources Research 10(6):1107–1112 11. Afshar A, Mariño M, Abrishamchi A (1991) Reservoir Planning for Irrigation District. Water Resources Planning and Management 117(1):74–85. 12. Mohan S, Raipure D (1992) Multiobjective Analysis of Multireservoir System. Water Resources Planning and Management 118(4):356–370 13. Li YP, Huang GH, Nie SL (2007) Mixed interval-fuzzy tow-stage integer programming and its application to flood-diversion planning. Engineering Optimization 39(2):163–183 14. Ahmad S, Prashar D (2010) Evaluating Municipal Water Conservation Policies Using a Dynamic Simulation Model. Water Resources Management 24:3371–3395 15. Chang FC, Hui SC (1998) Grey Fuzzy Stochastic Dynamic Programming for Reservoir Operation. Journal of Chinese Agricultural Engineering 44(1):34–49 16. Chou NF, Lee HC (2011) Optimal Scheduling of Regional Water Resources Developments. Chinese Agricultural Engineering 57(1):58–75 17. Darwin C (1859) On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. John Murray, London 18. Neumann V, Burks AW (1966) Theory of self-reproducing automata. IEEE Transactions on Neural Networks 5(1):3–14 19. Holland JH (1992) Genetic algorithms. Scientific american 267(1):66–73 20. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18(4):577–601 21. Hinton G, SalakhutdinovR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507 22. Vapnik V, Cortes C (1995) Support-vector networks. Machine learning 20(3): 273–297 23. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Inst for Cognitive Science, California Univ San Diego, La Jolla 24. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural computation 9(8):1735–1780 25. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412–3555 26. 葉克家 (2004) 新竹地區乾旱特性與農業用水調度之研究 (II) 27. 朱吟晨、林士堯、朱容練、劉俊志、陳永明 (2015) 2015年乾旱事件分析,國家災害防救科技中心 28. 張東興 (1999) 新竹地區水源調配利用之研究,國立海洋大學,基隆市 29. 魏志強 (1998) 線性規劃法與網流法應用於地表水系統水權模擬之比較,博士論文,國立台灣大學,台北市 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84094 | - |
dc.description.abstract | 台灣近年受氣候變遷影響乾旱日趨顯著,台灣年總降雨量雖然豐沛,但降雨時間分布不均,尤以台灣南部地區之河川流量豐枯比約為9:1最為嚴重,此外南部地區水資源系統水源調配不均的問題,使區域水資源調度壓力更形加劇,水資源短缺發生的頻率和幅度將在未來逐漸增加,因此未來長期入流量的推估、水庫最佳化操作和水資源跨區整合調度為水資源有效利用重要且迫切之課題。 台灣南部地區水資源互相支援規則複雜,為了水資源應用效益最大化,急需一個系統跨區整合調度。爰此,本研究提出一跨區水資源調度系統,採用人工智慧演算法,建構跨區水資源調度系統,包含區域供水比例推估以及多目標最佳化水庫操作策略制定。其中,供水比例推估模式建置採用支持向量機、多層感知器、深度神經網路、隨機森林演算法、長短期記憶、門閘遞迴單元共六種模式,使用評鑑指標及透過網格搜尋法尋找最佳的模式及其使用的因子與參數,回歸分析各因子與入流量的非線性關係,以學習區域水庫之供水行為,整合區域內水庫之蓄水量,並結合近年新穎的啟發式演算法之一,非支配排序遺傳演算法(Non-dominated Sorting Genetic Algorithm, NSGA-III),訂定水庫農業供水量最佳化規則,以制定包括公共、工業和農業用水及期末蓄水量等多目標最佳化水庫操作策略,應用長期入流量推估,提供未來六個月水資源逐日操作建議。 本研究以台灣南部地區嘉義、台南以及高雄水資源系統驗證模式之準確度。首先解析南區水資源的調度原則,在既有原則下以NSGA-III和人工智慧理論,提出最佳化操作策略,並將水資源調度系統調控之成果與歷史數據對比分析。結果顯示,區域供水比例推估模式以SVM表現為最佳,能準確模擬台南地區實際供水行為。以2014枯水年嘉南灌區調度成果為例,根據NSGA-III演算結果制定之最佳化操作策略,可改善當年歷史灌溉供水量不足9149.5萬噸狀況,且動態調控期末供水量較歷史紀錄增加約6448.3萬噸,供水量提升51.4%,期末蓄水量亦可蓄留7122.7萬噸,達蓄水量管控目標。 本研究提出之跨區水資源調度系統,可提供未來六個月跨區和跨標的之水資源調度建議,並提前示警水資源缺乏,建議農業停灌,透過調度減少缺水導致的不便,面對未來異常氣候之挑戰,期待提高水資源使用效益,並提供相關單位作為水資源調度決策的參考。 | zh_TW |
dc.description.abstract | In recent years, the influence of climate-related issues such as climate change and droughts on Taiwan has become increasingly significant. Taiwan has been suffering from the non-uniform distribution of precipitation in time, despite the total annual precipitation is abundant. The issue is serious particularly in southern Taiwan which the flow in the wet season accounts for 90% of the total flow. Furthermore, the imbalanced distribution of water resources in southern Taiwan leads to difficulties in allocating water resources. Since the frequency and intensity of water shortage will gradually increase, the long-term simulation in inflow, reservoir optimization, and allocation of cross-region water resources are necessary for effective water resources utilization. The regulations of supportive water resource allocation in southern Taiwan are complicated. In order to maximize the benefit of regional water allocation, this study presents a cross-region water resources allocation system that constructed by artificial intelligence. The system can not only simulate the water supply strategies of multi-reservoir, but also develop the multi-objective optimization operations to trade-off between the domestic, industry, agricultural water demands, and reservoir storage. Artificial intelligence methods/algorithms used in this study to construct simulation model include support vector machine (SVM), multilayer perceptron, deep neural network, random forest, long short-term memory, and gates recurrent unit. Gridsearch is used to calibrate the factors and parameters of the models, and the best model is determined according to the performance measures. Then, the cross-region water resources allocation system integrates the best model mentioned above and Non-dominated Sorting Genetic Algorithm (NSGA-III) to set optimal agricultural water supply regulations. The system can provide every-day operation recommendations within a six-month period with the long-term streamflow forecasting. In this study, the water resources systems of Chiayi, Tainan, and Kaohsiung are chosen to verify the accuracy of the cross-region water resources allocation system. The results of this study include the following three parts: Firstly, the water resources allocation principles are analyzed. Secondly, an optimal operation strategy based on NSGA-III under the existing limitations is proposed. Finally, we compare the results of water resource allocation system with historical data. The results show that the performance of SVM model is the best. Take the allocation result of Chianan irrigation area in 2014 for instance, the proposed operation can improve the situation in which irrigation water supply was insufficient for about 91.5 megaton. In this case, the suggested water supply from the model is a 64.5 megaton increase from historical record. That is, rise by 51.4%. Simultaneously, the proposed water storage is 31.2 megaton more than the water storage control objective. The allocation system for cross-region water resources can propose operating recommendations within a six-month period to mitigate the inconvenience caused by water shortage, warn the insufficiency of water resources in advance, and propose the suggestions of suspending irrigation. In conclusion, the model proposed in this study can improve the efficiency of water resources utilization and assist local agencies take on the challenges of abnormal climate in the future. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T22:04:42Z (GMT). No. of bitstreams: 1 U0001-1507202216405300.pdf: 16742792 bytes, checksum: d35603f0119062e32a700d4d3a585e85 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | 摘要 II Abstract IV 目錄 VI 圖目錄 IX 表目錄 XIII 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 3 1.2.1基因演算法於水資源系統分析的應用 3 1.2.2 水庫系統最佳化操作 4 1.3 論文架構 6 第二章 研究區域與資料 7 2.1 研究區域概述 7 2.1.1 嘉義地區 7 2.1.2 台南地區 9 2.1.3 高雄地區 12 2.2 流量推估資料 15 第三章 研究方法 18 3.1 非支配排序遺傳演算法 18 3.1.1 基因演算法 18 3.1.2 多目標最佳化問題 24 3.1.3 NSGA-III 26 3.2 人工智慧演算法 31 3.2.1 機器學習法 31 3.2.2 深度學習法 34 第四章 模式建立與應用 37 4.1 水資源調度系統 39 4.1.1模式概述 39 4.1.2模式限制式 41 4.1.3模式調度原則 43 4.1.4 台南地區水庫供水比例 52 4.2 最佳化操作訂定 55 4.2.1最佳化方案 55 4.2.2目標函數 58 4.3 評鑑指標 60 4.3.1 NSGA-III 60 4.3.2人工智慧演算法 61 第五章 結果與討論 62 5.1 台南地區水庫供水比例 62 5.2 NSGA-III調度方案 65 5.3 最佳化操作驗證 70 5.3.1豐水年 71 5.3.2枯水年 77 5.4動態調控應用 82 5.4.1豐水年 82 5.4.2枯水年 97 5.4.3平水年 111 第六章 結論與建議 124 6.1 結論 124 6.2 建議 126 參考文獻 127 | |
dc.language.iso | zh-TW | |
dc.title | 新型跨區水資源調度系統之研究 | zh_TW |
dc.title | Study on a novel cross-region water resources allocation system | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李方中(Fang-Zhong Li),賴進松(Jin-Song Lai) | |
dc.subject.keyword | 水資源調度系統,跨區調度,人工智慧,非支配排序遺傳演算法,最佳化操作, | zh_TW |
dc.subject.keyword | water resources allocation system,cross-regional allocation,artificial intelligence,NSGA-III,optimal operation, | en |
dc.relation.page | 129 | |
dc.identifier.doi | 10.6342/NTU202201482 | |
dc.rights.note | 同意授權(限校園內公開) | |
dc.date.accepted | 2022-07-19 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
dc.date.embargo-lift | 2022-09-06 | - |
顯示於系所單位: | 土木工程學系 |
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