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研究生:李明軒
研究生(外文):Ming-xaun Li
論文名稱:支撐向量機與模糊推論於流量預報即時誤差修正之研究
論文名稱(外文):The Study on Real-time Error Correction on Flow Forecasting with Support Vector Machine and Fuzzy Inference Model
指導教授:游保杉游保杉引用關係
指導教授(外文):Pao-Shan Yu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:水利及海洋工程學系碩博士班
學門:工程學門
學類:河海工程學類
論文種類:學術論文
畢業學年度:96
語文別:中文
論文頁數:113
中文關鍵詞:支撐向量機模糊推論模式分佈型水文模式即時誤差修正微基因演算法
外文關鍵詞:fuzzy inference modelSupport Vector Mechinedistributed hydrological modelmicro-Genetic Algrithmreal-time error correction
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  • 被引用被引用:12
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本研究結合QPESUMS (Quantitative Precipitation Estimation- Segregation Using Multiple Sensor) 雷達雨量系統與分佈型水文模式建構空間流量預報模式,利用未來1 ~ 3小時雨量推估1 ~ 3小時流量預報值,並建立兩種即時誤差修正的方法以提升流量預報之精確程度。本研究以基隆河五堵流量站上游為研究流域,挑選2005 ~ 2006年總共10場颱洪事件,其中7場作為模式率定,其餘3場則作為模式驗證及流量預報之研究。在模式參數率定方面,本文利用微基因演算法(μGA)自動搜尋最佳參數以縮短搜尋時間,並參考參數物理意義限制其範圍後進行率定。此外,研究亦發現並聯率定(各事件獨立率定)後其參數參數平均值與所有事件串聯率定(依照時間順序),所得結果發現差異不大。在模式即時更新方面,本研究利用支撐向量機(Support Vector Machine)及模糊推論模式(Fuzzy Inference Model)兩種方法建立即時誤差修正模式以修正未來流量預報值。結果發現空間逕流預報模式結合即時誤差修正系統可以達到提升預報精度之目的,但隨著前置時間(Lead time)增長提升之幅度有隨之下降之趨勢。另外,研究結果也發現支撐向量機所建立之即時誤差修正模式,在各個前置時間及所有評鑑指標中,絕大部分皆優於模糊推論模式所建立之誤差修正模式。
This study developed a spatial flow forecasting model by integration of the QPESUMS (Quantitative Precipitation Estimation Segregation Using Multiple Sensor) system with distributed rainfall-runoff model to provide 1-3 hours ahead flow forecasts. Two real-time error correction models were also included to improve the performance of flow forecasting. The up-stream of Wu-Tu flow gauge in Kee-Lung River is used as study area. Ten historical storms occurred during 2006~2007 are chosen as the data bases, in which seven storm events are used for model calibration and another three events are used for model verification. The Micro-Genetic Algorithm (μGA) is utilized for automatic parameters calibration and the searching domain is reduced by the physical property of basin. Furthermore, the study revealed that there are no significant differences between parallel connections calibration (calibrated respectively) and series connections calibration (connected all events and calibrated). In real-time updating, the fuzzy inference method and support vector machine (SVM) are applied to modify the flow forecasts in real time. The results reveal that the integration of real-time error correcting model and spatial runoff forecasting model can improve flow forecasting, however the accuracy still decrease with increase of lead times. The results also showed that SVM method has better performance than fuzzy inference method in most criteria. It seems that SVM is suitable to construct the real-time error correcting model and is able to improve the accuracy of flow forecasting.
目 錄.......................................................I
表 目 錄.....................................................V
圖 目 錄...................................................VII

第一章 緒論....................................................1
1-1 研究動機與目的......................................1
1-2 文獻回顧............................................2
1-3 本文組織............................................5

第二章 研究流域介紹與水文模式之基本理論........................9
2-1 研究流域概述........................................9
2-2 流域雨量資料........................................9
2-3 降雨-逕流模式之建立................................11
2-4 降水損失...........................................13
2-4-1 修正荷頓(Horton)公式.........................13
2-4-2 入滲參數推算.................................15
2-5 水流基本方程式.....................................17
2-5-1 漫地流演算...................................17
2-5-2 渠道流演算...................................20
2-6員山子分洪演算說明..................................21

第三章 水文模式參數率定與驗證.................................33
3-1 基因演算法.........................................33
3-1-1 基因演算法流程...............................34
3-1-2 微基因演算法.................................38
3-2 模式率定與驗證.....................................40
3-2-1 事件並聯之率定與驗證.........................41
3-2-2 事件串連之率定與驗證.........................43

第四章 結合雨量預報於流量預報.................................63
4-1 雨量預報...........................................63
4-2 流量預報...........................................64
4-3 預報誤差討論.......................................65

第五章 流量預報即時誤差修正...................................71
5-1 支撐向量機之誤差修正...............................71
5-1-1 結構風險最小化...............................71
5-1-2 支撐向量迴歸.................................73
5-1-3 支撐向量迴歸(SVR)之率定......................77
5-1-4支撐向量機之誤差預測修正結果..................78
5-2 模糊推論模式之誤差修正.............................79
5-2-1 糢糊化介面...................................80
5-2-2 模糊規則庫...................................80
5-2-3 模糊推論引擎.................................81
5-2-4 解糢糊化.....................................82
5-2-5 模糊推論之誤差預測修正結果...................82
5-3 模糊推論模式及支撐向量機之流量預報誤差修正之比較...83

第六章 結論與建議............................................101
6-1 結論..............................................102
6-2 建議..............................................103

參考文獻.....................................................105
王如意,「空間分布地表逕流多層核胞模式之研究及其應用」,行政院農業委員會計畫報告,1989a。
王如意,「台北都會區淹水區域預測之研究-子計畫二:都會郊區降雨–逕流之研究(I)」,行政院國家科學委員會研究計畫報告,1998b。
王如意、何輔仁、謝平城,「坡地集水區分布型降雨–逕流模式之研究」,台灣水利,第42卷,第4期,第1-20頁,1994b。
王如意、李戎威,「核胞模式之理論研析及其應用於流域之洪水演算」,農業工程學報,第35卷,第4期,第1-23頁,1989b。
王如意、鄭士仁等,「臺北防洪整體檢討計畫(一)」,經濟部水資源局研究計劃報告,1995。
王如意、謝平城、何輔仁,「以克利金法應用於坡地集水區水文分析之研究」,行政院農業委員會計畫報告,1994a。
王如意、謝龍生、嚴玉書,「以類神經網路模式分析颱風降雨與半分布並聯式水庫概念模式模擬颱洪歷線之串聯應用」,農業工程學報,第44卷,第2期,第1-25頁,1998a。
王毓麒,「分佈型水文模式於洪災消減及未量測集水區逕流歷線之推估」,博士論文,國立成功大學水利及海洋工程研究所,2006。
李光敦、鍾逸茹、黃仁國,「集水區暴雨尺度對逕流之非線性關係分析」,農業工程學報,第52卷,第4期, 2006。
張逸凡,「支撐向量機在即時河川水位預報之應用」,碩士論文,國立成功大學水利及海洋工程研究所,2005。
郭振民,「應用遙測與地理資訊系統於分佈型降雨-逕流模式之研究」,碩士論文,國立成功大學水利及海洋工程研究所,1999。
陳信彰,「分佈型降雨-逕流模式之不確定性與敏感度分析」,碩士論文,國立成功大學水利及海洋工程研究所,1997。
陳憲宗,「Real-time Probabilistic Flood Stage Forecasting Using Support Vector Machines and Fuzzy Inference Model」,博士論文,國立成功大學水利及海洋工程研究所,2006。
陳憲宗、張逸凡、謝章廷、游保杉,「支撐向量機制:洪水水位預報模式」,台灣水利,第54卷,第2期,第50-61頁,2006。
陳憲宗、游保杉,「洪水位之即時機率預報 - 結合支撐向量機與模糊推理」,農業工程學報,第53卷,第4期,第1-20頁,2007。
劉光武,「分佈型降雨-逕流模式之研究」,碩士論文,國立成功大學水利及海洋工程研究所,1991。
鄭玉萩,「格網式分佈型降雨-逕流模式之研究」,碩士論文,國立成功大學水利及海洋工程研究所,1995。
謝章廷,「應用雷達降雨於分佈型水文模式與不確定性分析」,碩士論文,國立成功大學水利及海洋工程研究所,2007。
顏清連等,「臺北都會區大眾捷運系統防洪排水設計之研究」,國立台灣大學水工試驗所研究報告第100號,1989。
「基隆河員山子分洪計畫工程基本設計報告」,2002年5月,經濟部水利署水利規劃試驗所出版,2002。
「基隆河員山子分洪水工模型試驗(第一年)」,2002年10月,經濟部水利署水利規劃試驗所出版,2002。
Abrahart R.J., and See, L., “Multi-model Data Fusion for River Flow Forecasting: An Evaluation of Six Alternative Methods Based on Two Contrasting Catchments”, Hydrology and Earth System Sciences, 6(4), 655-670, 2002.
Anderson, M. L., Z.-Q. Chen, M. L. Kavvas, and Arlen Feldman, “Coupling HEC-HMS with Atmospheric Models for Prediction of Watershed Runoff”, Journal of Hydrologic Engineering, 7(4), 312-318, 2002.
Bazartseren, B., Hildebrandt, G., and Holz, K.-P., “Short-term water level prediction using neural networks and neuro-fuzzy approach”, Neurocomputing, 55, 439-450, 2003.
Brath, A., A. Montanari, and E. Toth, “Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models”, Hydrology and Earth System Sciences, 6(4), 627-640, 2002.
Cannas, B., A. Montisci, A. Fanni, L. See, and G. M. Sechi, “Comparing Artificial Neural Networks and Support Vector Machines for Modelling Rainfall-Runoff”, In: Proceedings of the 6th International Conference on Hydroinformatics, S. Y. Liong, K. K. Phoon, and V. Babovic, eds., (Singapore, 21-24 June 2004), World Scientific Publishing Co., Singapore, 2004.
Chang, F.-J., Chen, Y.-C., and Liang, J.-M., “Fuzzy clustering neural network as flood forecasting model”, Nordic Hydrology, 33(4), 275-290, 2002.
Chang, F.-J., Hu, H.-F., and Chen, Y.-C., “Counterpropagation fuzzy-neural network for stream-flow reconstruction”, Hydrological Processes, 15(2), 219-232, 2001.
Cherkassky, V., and Y. Ma, “Practical selection of SVM parameters and noise estimation for SVM regression”, Neural Networks, 17, 113-126, 2004.
Chow,V.T., Maidment, D.R., and Mays,L.R., “Applied hydrology”, McGraw-Hill, New York, 1988.
Choy, K.Y., and Chan, C.W., “Modelling of river discharges and rainfall using radial basis function networks based on support vector regression”, International Journal of Systems Science, 34(14-15), 763-773, 2003.
Collier, C. G., “Weather radar precipitation data and their use in hydrological modeling”, In: Distributed Hydrological Modeling (ed. by M. B. Abbott and J. C. Refsgaard), Ch. 8, 143-163. Kluwer Academic Publishers, Dordrecht, The etherlands, 1996.
Collischonn, W., Carlos E. M. T., Robin T. C., Sin C. C., Luiz G. G., Márcio C., and Daniel A., “Medium-range reservoir inflow predictions based on quantitative precipitation forecasts”, Journal of Hydrology, 344, 112-122, 2007.
Collischonn, W., Reinaldo H., Ivanilto A., and Carlos E. M. T., “Forecasting River Uruguay flow using rainfall forecasts from a reginal weather-prediction model”, Journal of Hydrology, 305, 87-98, 2005.
Corral, C., D. Sempere-Torres, M. Revilla, and M. Berenguer, “A Semi-Distributed Hydrological Model Using Rainfall Estimates by Radar. Application to Mediterranean Basins”, Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25, 1133-1136, 2000.
De Jong, K. A., “An analysis of the behavior of a class of genetic adaptive systems”, Ph.D. dissertation, Univ. Michigan, Ann Arbor, 1975.
De Jong, K. A., and W.M. Spears, “On the virtues of parameterized uniform crossover”, In Proceedings of 4th International Conference on Genetic Algorithms, La Jolla, CA, 230-236, 1991.
Dibike, Y.B., Velickov, S., Solomatine, D., and Abbott, M.B., “Model induction with support vector machines: Introduction and applications”, Journal of Computing in Civil Engineering, 15(3), 208-216, 2001.
Engman, E.T., and A.S. Rogowski, “A partial area model for storm flow synthesis”, Water Resources Research, 10(3), 464-472, 1974.
Fletcher, R., Practical Methods of Optimization, John Wiley and Sons, New York, 1987.
Goldberg, D.E., “Genetic algorithm in Search. Optimization and Machine Learning”, Addison-Wesley, U.S., 1989.
Goldberg, D.E., and F.G. Lobo, “The parameter-less genetic algorithm in practice”, IlliGAL Report NO. 2001022, 2001.
Goswami, M., K.M. O'Connor, K.P. Bhattarai, and A.Y. Shamseldin, “Assessing the performance of eight real-time updating models and procedures for the Brosna River”, Hydrology and Earth System Sciences, 9(4), 394-411, 2005.
Grefenstette, J. J., “Optimization of control parameters for genetic algorithms”, IEEE Trans. Syst., Man Cybern., SMC-16(1), 122-128, 1986.
Habets, F., Patrick L., and Joël N., “On the utility of operational precipitation forecasts to served as input for stream-flow forecasting”, Journal of Hydrology, 293, 270-288, 2004.
Holland, J.H., “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Arbor, MI., 1975.
Hsu, C. W., C. C. Chang, and C. J. Lin, “A Practical Guide to Support Vector Classification”, Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Available at: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf, 2003.
Huggins, L.F., and E.J. Monke, “A mathematic model for simulating the hydrologic response of a watershed”, Water Resources Research, 4(3), 529–539, 1968.
Hundecha, Y., Bárdossy, A., and Theisen, H.W., “Development of a fuzzy logic-based rainfall-runoff model”, Hydrological Sciences Journal, 46(3), 363-376, 2001.
Ibbitt, R.P., R.D. Henderson, J. Copeland, and D.S. Wratt, “Simulating mountain runoff with meso-scale weather model rainfall estimates: a New Zealand experience”, Journal of Hydrology, 239, 19-32, 2001.
Jasper, K., Joachim Gurtz, and Herbert Lang, “Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model”, Journal of Hydrology, 267, 40-52, 2002.
Jonch-Clausen, T., “Système Hydrologique Européen, A Short Description”, Danish Hydraulic Institute, 1979.
Koussis1, A. D., Konstantinos L., Katerina M., Vassiliki K., Dieter S., Jürgen L., Hermann Z., Andrea B., and Piero M., “Flood Forecasts for Urban Basin with Integrated Hydro-Meteorological Model”, Journal of Hydrologic Engineering, 8(1), 1-11, 2003.
Krishnakumar, K., “Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization”, SPIE Intelligent Control and Adaptive Systems, 1196, 289-296, 1989.
Kutchment, L.S., “A two-dimensional rainfall-runoff model: identification of parameter and possible use for hydrological forecasts”, In: Hydrological Forecasting, IAHS Publication 129, 215–219, 1980.
Lee, C.C., “ Fuzzy logic in control systems: Fuzzy logic controller, Part II”, IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 419-435, 1990.
Lin, C. J., and co-workers, LIBSVM Software (Version 2.71, released on November 20, 2004), Available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html, 2004.
Liong, S.-Y., and Sivapragasam, C., “Flood stage forecasting with support vector machines”, Journal of the American Water Resources Association, 38(1), 173-196, 2002.
Madsen, H., and Skotner, C., “Adaptive state updating in real-time river flow forecasting - a combined filtering and error forecasting procedure”, Journal of Hydrology, 308, 302-312, 2005.
Mahabir, C., Hicks, F.E., and Fayek, A.R., “Application of fuzzy logic to forecast seasonal runoff”, Hydrological Processes, 17, 3749-3762, 2003.
Philip B. Bedient, Anthony Holder, Jude A. Benavides, and Baxter E. Vieux , “Radar-Based Flood Warning System Applied to Tropical Storm Allison”, Journal of Hydrologic Engineering, 8(6), 308-318, 2003.
Ross, B.B., D.N. Contractor, and V.O. Shanholz, “A finite element model for overland and channel flow for assessing the hydrological impact of land-use change”, Journal of Hydrology, 41, 11-30, 1979.
Rovey E.W., D.A. Woolhiser, and R.E. Smith, “A distributed kinematic model of upland watershed”, Hydrology Paper 93, Colorado State University, Fort Collins, Colorado, 1977.
See, L., and Openshaw, S., “A hybrid multi- model approach to river level forecasting”, Hydrological Sciences Journal, 45(4), 523-536, 2000.
Singh V. P., J. M. Hill, and H. Aminian, “A Computerized Data Base for Flood Prediction Modeling”, Water Resources Bulletin, 23, No. 1, 21-27, 1987.
Sivapragasam, C., and Liong, S.-Y., “Flow categorization model for improving forecasting”, Nordic Hydrology, 36(1), 37-48, 2005.
Sivapragasam, C., Liong, S.-Y., and Pasha, M.F.K., “Rainfall and runoff forecasting with SSA-SVM approach”, Journal of Hydroinformatics, 3(3), 141-152, 2001.
Smith, R.E., and D.A. Woolhiser, “Overland flow on an infiltrating surface”, Water Resources Research, 7(4), 899-913, 1971.
Soil Conservation Service, “National Engineering Handbook, Part I -Watershed Planning in Hydrology”, 1964.
Solomatine, D.P., “Genetic and other global optimization algorithms-comparison and use in calibration problems”, Proc. 3rd Intern. Conference on Hydroinformatics, Copenhagen, Denmark, 1998.
US Army Corps of Enginners Hydrologic Engineering Center, “Hydrologic System Hec-HMS Technical Reference Manual”, 2000.
Vapnik, V.N., The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
Vapnik, V.N., Statistical Learning Theory, Wiley, New York, 1998.
Vojinovic, Z., and V. Kecman, Contaminant Transport Modelling with Support Vector Machine Model - An Alternative to Classical Advection-Dispersion Equation”, In: Proceedings of the 6th International Conference on Hydroinformatics, S. Y. Liong, K. K. Phoon, and V. Babovic, eds., (Singapore, 21-24 June 2004), World Scientific Publishing Co., Singapore, 2004.
Wang Q. J., “Using genetic algorithms to optimise model parameters”, Environ Model Software, 12(1), 27-34, 1997.
Whitley, D., “The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trial is best”, Proceeding of 3rd International Conference on Genetic Algrithms, 116-121, 1989.
WMO, “Simulated Real-time Intercomparison of Hydrological Models”, WMO Operational Hydrological Report No. 38, Geneva, Switzerland 241, 1992.
Xiong, L., Shamseldin, A.Y., and O'Connor, K.M., “A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system”, Journal of Hydrology, 245, 196-217, 2001.
Yu, P.S., “Real-Time Grid Based Distributed Rainfall-Runoff Model for Flood Forecasting with Weather Radar”, PhD Thesis, University of Birmingham, 1987.
Yu, P.-S., and Chen, S.-T., “Updating real-time flood forecasting using a fuzzy rule-based model”, Hydrological Sciences Journal, 50(2), 265-278, 2005.
Yu, P.-S., Chen, S.-T., and Chang, I.-F., “Support vector regression for real-time flood stage forecasting”, Journal of Hydrology, 328(3-4), 704-716, 2006.
Yu, X., and Liong, S.-Y., “Forecasting of Chaotic Hydrological Time Series with Ridge Linear Regression in Feature Space”, In: Proceedings of the 6th International Conference on Hydroinformatics, S. Y. Liong, K. K. Phoon, and V. Babovic, eds., (Singapore, 21-24 June 2004), World Scientific Publishing Co., Singapore, 2004.
Yu, X., Liong, S.-Y., and Babovic, V., “Hydrologic Forecasting with Support Vector Machine Combined with Chaos-Inspired Approach”, In: Hydroinformatics 2002: Proceedings of the 5th International Conference on Hydroinformatics, (Cardiff, U.K., 1-5 July 2002), IWA Publishing, London, U.K., 764-769, 2002.
Yu, X., Liong, S.-Y. and Babovic, V., “EC-SVM approach for real-time hydrologic forecasting”, Journal of Hydroinformatics, 6(3), 209-223, 2004.
Yu, Z., M.N. Lakhtakia, B. Yarnal, R.A. White, D.A. Miller, B. Frakes, E.J. Barron, C. Duffy and F.W. Schwartz, “Simulating the river-basin response to atmospheric forcing by linking a mesoscale meteorological model and hydrologic model system”, Journal of Hydrology, 218, 72-91, 1999.
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