請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43191完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張斐章 | |
| dc.contributor.author | Yi-Hua Ho | en |
| dc.contributor.author | 何宜樺 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:41:45Z | - |
| dc.date.available | 2010-09-01 | |
| dc.date.copyright | 2009-07-28 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-13 | |
| dc.identifier.citation | 1.Antar, M.A., Elassiouti, I. and Allam, M.N., 2006. Rainfall-runoff modelling using artificial neural networks technique: a Blue Nile catchment case study. Hydrological Processes, 20(5): 1201-1216.
2.Campolo, M., Andreussi, P. and Soldati, A., 1999. River flood forecasting with a neural network model. Water Resources Research, 35(4): 1191-1197. 3.Campolo, M., Soldati, A. and Andreussi, P., 2003. Artificial neural network approach to flood forecasting in the River Arno. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 48(3): 381-398. 4.Chang, F.J., Chang, L.C. and Huang, H.L., 2002a. Real-time recurrent learning neural network for stream-flow forecasting. Hydrological Processes, 16(13): 2577-2588. 5.Chang, F.J., Chang, L.C. and Wang, Y.S., 2007. Enforced self-organizing map neural networks for river flood forecasting. Hydrological Processes, 21(6): 741-749. 6.Chang, F.J. and Chang, Y.T., 2006. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 29(1): 1-10. 7.Chang, F.J. and Chen, Y.C., 2001. A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology, 245(1-4): 153-164. 8.Chang, F.J., Chen, Y.C. and Liang, J.M., 2002b. Fuzzy clustering neural network as flood forecasting model. Nordic Hydrology, 33(4): 275-290. 9.Chang, F.J. and Hwang, Y.Y., 1999. A self-organization algorithm for real-time flood forecast. Hydrological Processes, 13(2): 123-138. 10.Chang, F.J., Liang, J.M. and Chen, Y.C., 2001. Flood forecasting using radial basis function neural networks. Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 31(4): 530-535. 11.Chang, L.C. and Chang, F.J., 2001. Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9): 1621-1634. 12.Chang, L.C., Chang, F.J. and Chiang, Y.M., 2004. A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrological Processes, 18(1): 81-92. 13.Chang, L.C., Chang, F.J. and Tsai, Y.H., 2005. Fuzzy exemplar-based inference system for flood forecasting. Water Resources Research, 41(2): 12. 14.Chen, S.H., Lin, Y.H., Chang, L.C. and Chang, F.J., 2006. The strategy of building a flood forecast model by neuro-fuzzy network. Hydrological Processes, 20(7): 1525-1540. 15.Darken, P.F., Holtzman, G.I., Smith, E.P. and Zipper, C.E., 2000. Detecting changes in trends in water quality using modified Kendall's tau. Environmetrics, 11(4): 423-434. 16.Dawson, C.W. and Wilby, R., 1998. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 43(1): 47-66. 17.Dawson, C.W. and Wilby, R.L., 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography, 25(1): 80-108. 18.El-Shafie, A., Taha, M.R. and Noureldin, A., 2007. A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resources Management, 21(3): 533-556. 19.Esterby, S.R., 1996. Review of methods for the detection and estimation of trends with emphasis on water quality applications. Hydrological Processes, 10(2): 127-149. 20.Firat, M., 2008. Comparison of Artificial Intelligence Techniques for river flow forecasting. Hydrology and Earth System Sciences, 12(1): 123-139. 21.Firat, M. and Gungor, M., 2007. River flow estimation using adaptive neuro fuzzy inference system. Mathematics and Computers in Simulation, 75(3-4): 87-96. 22.Firat, M. and Gungor, M., 2008. Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrological Processes, 22(13): 2122-2132. 23.Gajiccapka, M., 1993. Fluctuations and trends of annual precipitation in different climatic regions of Croatia. Theoretical and Applied Climatology, 47(4): 215-221. 24.Hsu, K.L., Gupta, H.V. and Sorooshian, S., 1995. Artificial neural-network modeling of the rainfall-runoff process. Water Resources Research, 31(10): 2517-2530. 25.Jain, A. and Indurthy, S., 2003. Comparative analysis of event-based rainfall-runoff modeling techniques - Deterministic, statistical, and artificial neural networks. Journal of Hydrologic Engineering, 8(2): 93-98. 26.Jain, A., Sudheer, K.P. and Srinivasulu, S., 2004. Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrological Processes, 18(3): 571-581. 27.Jain, S.K., Das, A. and Srivastava, D.K., 1999. Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management-Asce, 125(5): 263-271. 28.Jang, J.-S.R. and Sun, C.-T., 1997. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc., 614 pp. 29.Jang, J.S.R., 1993. ANFIS - adaptive-network-based fuzzy inference system. Ieee Transactions on Systems Man and Cybernetics, 23(3): 665-685. 30.Kendall, M.G., 1938. A new measure of rank correlation. Biometrika, 30: 81-93. 31.Kendall, M.G., 1970. Rank correlation methods. Griffin, London :. 32.Maidment, D.R., 1993. Handbook of hydrology. McGraw-Hill, New York :. 33.Mamdani, E.H. and Assilian, S., 1975. Experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1): 1-13. 34.Mendel, J.M., 2001. Uncertain rule-based fuzzy logic systems : introduction and new directions. Prentice-Hall, Inc. 35.Nayak, P.C., Sudheer, K.P., Rangan, D.M. and Ramasastri, K.S., 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291(1-2): 52-66. 36.Partal, T. and Kahya, E., 2006. Trend analysis in Turkish precipitation data. Hydrological Processes, 20(9): 2011-2026. 37.Sudheer, K.P. and Jain, A., 2004. Explaining the internal behaviour of artificial neural network river flow models. Hydrological Processes, 18(4): 833-844. 38.Sugeno, M. and Kang, G.T., 1988. Structure identification of fuzzy model. Fuzzy Sets Syst., 28(1): 15-33. 39.Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. Ieee Transactions on Systems Man and Cybernetics, 15(1): 116-132. 40.Wu, J.S., Han, J., Annambhotla, S. and Bryant, S., 2005. Artificial neural networks for forecasting watershed runoff and stream flows. Journal of Hydrologic Engineering, 10(3): 216-222. 41.Xu, Z.X. and Li, J.Y., 2002. Short-term inflow forecasting using an artificial neural network model. Hydrological Processes, 16(12): 2423-2439. 42.Yue, S., Pilon, P. and Cavadias, G., 2002. Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology, 259(1-4): 254-271. 43.丁裕峰，2004，「併聯式類神經網路於水文事件之分析與應用」，國立台灣大學生物環境系統工程學研究所碩士論文。 44.王文中，2007，「統計學與Excel—資料分析之實習應用」，博碩文化。 45.王琮美，2005，「降雨條件與集水區出流量關係之探討」，國立中央大學土木工程研究所碩士論文。 46.江衍銘，2007，「類神經網路於水文氣象-以雷達及數值天氣預報資訊建構洪水預測」，國立台灣大學生物環境系統工程學研究所博士論文。 47.宋仁良，2004，「基隆河上游集水區地表特性與降雨逕流之關係研究」，中國文化大學地學研究所碩士論文。 48.李南慶，2007，「颱風影響集水區流域的降雨量與逕流量關係之研究」，淡江大學水資源及環境工程學系碩士論文。 49.林永堂，2004，「結合OLS 與SGA 建構輻狀基底類神經網路於洪水預測之研究」，國立台灣大學生物環境系統工程學研究所碩士論文。 50.周揚敬，2008，「應用支援向量機改善颱風期間水庫入流量預報」，國立台灣大學土木工程研究所碩士論文。 51.徐宏瑋，2004，「降雨量變遷趨勢檢定與分析」，國立台灣大學生物環境系統工程學研究所碩士論文。 52.許清炫，2005，「流域集水區降雨量與逕流量之分析與探討」，淡江大學水資源及環境工程學系碩士論文。 53.孫建平，1996，「類神經網路及其應用於降雨及逕流過程之研究」，國立台灣大學生物環境系統工程學系碩士論文。 54.張斐章、張麗秋，2005，「類神經網路」，東華書局。 55.蔡亞欣，2004，「模糊範例學習推論系統於水位預測之研究」，國立台灣大學生物環境系統工程學研究所碩士論文。 56.鄭家豪，2008，「改良式倒傳遞類神經網路於水庫入流量預報之研究」，國立台灣大學土木工程學研究所碩士論文。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43191 | - |
| dc.description.abstract | 颱風暴雨時期之水庫操作為水資源管理中相當重要的一環，因颱風往往造成大量洪水在短時間內自集水區匯流進入水庫中，若能提供精確之洪水預報資訊，則可有效提升水庫操作策略之可靠性，故水庫入流量之預報訊息，對於水庫操作的決策工作，具有極高的參考價值，因此本研究將以颱洪時期石門水庫入流量之多時刻預報為研究目標；研究內容主要分為兩部份，第一部份探討集水區降雨-逕流特性，針對雨量及入流量資料進行趨勢檢定，採用Pearson及Kendall相關係數以瞭解降雨及逕流歷線之間的線性及非線性關聯，並探討降雨的延遲特性。第二部份則根據第一部份之分析結果，加入相關性較高的雨量資訊，作為類神經網路輸入項，並以調適性網路模糊推論系統建立水庫入流量於颱風時期之多時刻預報模式；相關性分析結果顯示石門水庫集水區之平均雨量與入流量約有5∼7小時之稽延時間，而流量預報模式以預報時刻前5∼7小時之雨量及最近3小時之入流量作為輸入項，有最佳的預報結果，並可有效架構至未來1∼5小時之流量預報。對於多時刻的流量預報，除了以流量訊息作為輸入，加入適當的雨量資訊確實能提昇模式預測的精確性。 | zh_TW |
| dc.description.abstract | Reservoir operation is a prerequisite component for water resource management during typhoon periods. Due to the floods caused by typhoons often reach the reservoir within few hours, the need for a higher reliability of reservoir operation strategies is required. Therefore, an accurate hydrological model plays a key role and provides valuable flood information for reservoir operational decisions. The major purpose of this study is to construct a stable and reliable multistep ahead inflow forecasting of Shihmen reservoir. First of all, the characteristics of rainfall-runoff processes in this watershed are investigated by using trend detection methods for finding the relation between rainfall and runoff. In the second part, the artificial neural network (ANN), an effective data manipulation and prediction tool, is introduced in this study. The Adaptive Network-based Fuzzy Inference System (ANFIS) with model inputs consist of previous precipitation and streamflow information is developed for multistep ahead flood forecasting. The results indicate that both precipitation and flood patterns have the same trend with a shift of 5-7 hours. Accordingly, the ANFIS model provides accurate and effective flood forecasts to 5 hours-ahead. As far as the multistep ahead flood forecasting is concerned, appropriate combination of precipitation information indeed help to increase the accuracy of flood predictions. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:41:45Z (GMT). No. of bitstreams: 1 ntu-98-R96622001-1.pdf: 2532211 bytes, checksum: ed9a2f90cebce68624444072fbaa148e (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 摘 要 I
Abstract II 目 錄 III 表目錄 V 圖目錄 VI 第一章 緒論 1 1-1 研究動機 1 1-2 研究目的 2 1-3 論文架構 2 第二章 文獻回顧 3 2-1 降雨-逕流特性分析 3 2-2 類神經網路應用於水文預報模式 4 第三章 理論概述 8 3-1 相關性趨勢檢定 8 3-1-1 Kendall相關係數 8 3-1-2 Pearson相關係數 10 3-2 類神經網路 12 3-3 模糊推論系統 16 3-4 調適性網路模糊推論系統 19 第四章 研究案例 23 4-1 研究區域簡介 23 4-2 集水區降雨-逕流特性分析 26 4-3 多時刻洪水預報模式建構 35 第五章 預報結果與討論 38 5-1 流量模式 39 5-2 雨量模式 40 5-3 流量、雨量混合模式 41 5-4 流量、雨量改進模式 51 第六章 結論與建議 64 6-1 結論 64 6-2 建議 66 參考文獻 67 | |
| dc.language.iso | zh-TW | |
| dc.subject | 調適性網路模糊推論系統 | zh_TW |
| dc.subject | 多時刻流量預報 | zh_TW |
| dc.subject | 降雨-逕流模式 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | Adaptive Network-based Fuzzy Inference System | en |
| dc.subject | Multistep ahead flood forecasting | en |
| dc.subject | Artificial neural network | en |
| dc.subject | Rainfall-runoff modeling | en |
| dc.title | 建構集水區多時刻降雨-逕流機制與類神經網路洪水預報模式 | zh_TW |
| dc.title | Investigating Watershed Multistep Rainfall-Runoff Mechanisms and Modeling Flood Forecasting by Artificial Neural Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃文政,張麗秋,王藝峰,江衍銘 | |
| dc.subject.keyword | 降雨-逕流模式,調適性網路模糊推論系統,類神經網路,多時刻流量預報, | zh_TW |
| dc.subject.keyword | Rainfall-runoff modeling,Adaptive Network-based Fuzzy Inference System,Artificial neural network,Multistep ahead flood forecasting, | en |
| dc.relation.page | 73 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-14 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-98-1.pdf 未授權公開取用 | 2.47 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。