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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林國峰 | |
| dc.contributor.author | Chih-Wei Yang | en |
| dc.contributor.author | 楊志偉 | zh_TW |
| dc.date.accessioned | 2021-06-13T06:49:00Z | - |
| dc.date.available | 2007-08-01 | |
| dc.date.copyright | 2005-08-01 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-28 | |
| dc.identifier.citation | Cai, X.M., McKinney, D.C., Lasdon, L.S., 2001. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Advances in Water Resources 24(6), 667–676.
Chang, F.J., Chang, L.C., Huang, H.L., 2002. Real-time recurrent learning neural network for stream-flow forecasting. Hydrological Processes 16(13), 2577–2588. Chang, L.C., Chang, F.J., Chiang, Y.M., 2004. A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrological Processes 18(1), 81–92. Chiang, Y.M., Chang, L.C., Chang, F.J., 2004. Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling. Journal of Hydrology 290(3–4), 297–311. Chow, V.T., Maidment, D.R., Mays, L.W., 1988. Applied hydrology. McGraw-Hill, New York. Cooper, V.A., Nguyen, V.T.V., Nicell, J.A., 1997. Evaluation of global optimization methods for conceptual rainfall-runoff model calibration. Water Science and Technology 36(5), 53–60. Erickson, M., Mayer, A., Horn, J., 2002. Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA). Advances in Water Resources 25(1), 51–65. Goldberg, D.E., 1989. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Massachusetts. Haykin, S.S., 1999. Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River, N.J. Hsu, K.L., Gupta, H.V., Sorooshian, S., 1995. Artificial neural-network modeling of the rainfall-runoff process. Water Resources Research 31(10), 2517–2530. Isaaks, E.H., Srivastava, R.M., 1989 Applied geostatistics. Oxford University Press, New York. Kachroo, R.K., 1992. River flow forecasting .Part 1. A discussion of the principles. Journal of Hydrology 133(1–2), 1–15. Karunanidhi, N., Grenney, W.J., Whitley, D., Bovee, K., 1994. Neural networks for river flow prediction. Journal of computing in civil engineering 8(2), 201–220. Lallahem, S., Mania, J., 2003. A nonlinear rainfall-runoff Model using neural network technique: example in fractured porous media. Mathematical and Computer Modelling 37(9–10), 1047–1061. Lin, G.F., Chen, L.H., 2004. A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology 289(1–4), 1–8. Lischeid, G., Uhlenbrook, S., 2003. Checking a process-based catchment model by artificial neural networks. Hydrological Processes 17(2), 265–277. Luk, K.C., Ball, J.E., Sharma, A., 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. Journal of hydrology, 227(1–4), 56–65. Michalewicz, Z., 1996. Genetic algorithms + data structures = evolution programs. Springer-Verlag, New York. Minns, A.W., Hall, M.J., 1996. Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques 41(3), 399–419. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models .1. A discussion of principles. Journal of Hydrology 10, 282–290. Rajurkar, M.P., Kothyari, U.C., Chaube, U.C., 2004, Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology 285(1–4), 96–113. Reeves, C., 1997. Genetic Algorithms for the Operations Researcher, Informs Journal of Computing 9(3), 231–250. Sajikumar, N., Thandaveswara, B.S., 1999. A non-linear rainfall-runoff model using an artificial neural network. Journal of Hydrology 216(1–2), 32–55. Singh, V.P., 1995. Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, Colo. Smith, J., Eli, R.N., 1995. Neural Networks models of rainfall-runoff process. Journal of Water Resources Planning and Management ASCE 121(6), 499–508. Sugawara, M., 1995. Tank model in: Editor, Singh. V. J. (Ed.). Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, CO, USA. Walpole, R.E., Myers, R.H., Myers, S.L., 1998. Probability and statistics for engineers and scientists. Prentice Hall International, London. Wang, C.M., A nonlinear rainfall-runoff model embedded with an automated calibration method .Part 1. The model (unpublished). Wang, C.M., A nonlinear rainfall-runoff model embedded with an automated calibration method .Part 2. Automated calibration method (unpublished). Wang, C.M., A nonlinear rainfall-runoff model embedded with an automated calibration method .Part 3. Applications (unpublished). Wang, Q.J., 1997. Using genetic algorithms to optimise model parameters. Environmental Modeling & Software 12(1), 27–34. 王如意、易任，1979，應用水文學上、下冊，國立編譯館。 菅原正已，1985，水文水櫃模式分析技術研習會講義，國立台灣大學土木工程學研究所。 江得榮，2001，逕流預報模式回饋演算之研究，國立台灣大學土木工程學研究所碩士論文。 李光敦，2002，水文學，五南圖書出版股份有限公司。 張斐章，張麗秋，黃浩倫，2003，類神經網路理論與實務，台灣東華書局股份有限公司。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35346 | - |
| dc.description.abstract | 降雨─逕流過程是一個高度複雜和非線性的物理現象，導致模擬降雨─逕流過程相當困難，於是乎研究者相當迫切需要可以精準模擬、方便使用的降雨─逕流模式。非線性計算單元串聯模式（Nonlinear computational unit cascaded model, NCUC model）吸取以往經常被應用在降雨─逕流的水筒模式或是類神經網路等等模式的優點並避免有這些模式的缺點，比如內建參數自動檢定功能，可以輕鬆取得模式參數；並可視需求調整模式架構及內部設定參數，自由性相當高；輸入資料也只需要降雨資料即可。本研究以翡翠水庫集水區為研究對象，非線性計算單元串聯模式為架構，以求適切反映集水區的地表逕流與地下水流。參數檢定方法為模式內建的簡單型遺傳演算法（simple genetic algorithms, SGA），並選用弗雷特颱風（FRED）以及葛拉絲颱風（GLADYS）進行模式的參數檢定；寶莉颱風（POLLY）與泰德颱風（TED）進行模式的參數驗證。目標函數使用簡單最小平方法（simple least square）與效率係數（efficiency coefficient）。本研究將分別比較2、3與4個非線性計算單元（Nonlinear computational unit, NCU），並且探討將不同數目的非線性計算單元加以串聯與不同目標函數對模擬結果的影響。結果顯示，不論以簡單最小平方法或是效率係數為目標函數的檢定與驗證的結果都相當不錯，洪峰流量誤差與總逕流體積誤差可以在10％之內，均方根誤差在檢定時平均為50，驗證時平均為80，效率係數與決定係數在檢定時均有95％以上的水準，驗證時也可以到達90％以上。同時從研究結果中也可以得到當模式中非線性計算單元串聯的數目越多時，可以提高模擬結果更好的可能性，並且最後從結果中可以驗證非線性計算單元串聯模式在降雨─逕流過程的模擬上有相當好的成效。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-13T06:49:00Z (GMT). No. of bitstreams: 1 ntu-94-R92521326-1.pdf: 2047726 bytes, checksum: 9c8a60558339997996b6f0fff8ae9524 (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | 第一章 緒論.................................1
1-1 研究動機.............................1 1-2 前人研究.............................2 1-2-1 傳統概念型模式─水筒模式....2 1-2-2 類神經網路..................3 1-3 章節介紹..............................9 第二章 模式理論............................11 2-1 降雨─逕流模式......................11 2-1-1 模式之選擇.................11 2-1-2 非線性計算單元串聯模式.....13 2-2 參數優選............................17 第三章 降雨─逕流模式之建立................21 3-1 研究對象之概述......................21 3-2 水文資料之整理......................22 3-3 模式參數之檢定......................23 3-4 目標函數之建立......................26 3-5 參數檢定之上下界....................28 第四章 模式校驗及結果......................29 4-1 模式之成果評估......................29 4-2 模式組態之比較......................32 第五章 結論與建議..........................35 5-1 結論................................35 5-2 建議................................36 參考文獻.....................................39 | |
| dc.language.iso | zh-TW | |
| dc.subject | 簡單型遺傳演算法 | zh_TW |
| dc.subject | 非線性計算單元串聯模式 | zh_TW |
| dc.subject | 逕雨─逕流 | zh_TW |
| dc.subject | 模擬 | zh_TW |
| dc.subject | simulation | en |
| dc.subject | Nonlinear computational unit cascaded model | en |
| dc.subject | rainfall-runoff | en |
| dc.subject | simple genetic algorithms | en |
| dc.title | 非線性計算單元串聯模式於降雨─逕流模式之應用 | zh_TW |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳明杰,林文欽 | |
| dc.subject.keyword | 非線性計算單元串聯模式,逕雨─逕流,模擬,簡單型遺傳演算法, | zh_TW |
| dc.subject.keyword | Nonlinear computational unit cascaded model,rainfall-runoff,simulation,simple genetic algorithms, | en |
| dc.relation.page | 138 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-07-29 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| Appears in Collections: | 土木工程學系 | |
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| ntu-94-1.pdf Restricted Access | 2 MB | Adobe PDF |
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