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系統識別號 U0026-2206201116272200
論文名稱(中文) 克利金法即時修正大甲溪雷達估計降雨
論文名稱(英文) Real-time Correction of Radar Precipitation by using Kriging Method in Dajia River Basin
校院名稱 成功大學
系所名稱(中) 水利及海洋工程學系碩博士班
系所名稱(英) Department of Hydraulics & Ocean Engineering
學年度 99
學期 2
出版年 100
研究生(中文) 陳薇伊
研究生(英文) Wei-Yi Chen
學號 n86984103
學位類別 碩士
語文別 中文
論文頁數 91頁
口試委員 指導教授-游保杉
口試委員-鄭克聲
口試委員-李光敦
口試委員-徐國錦
中文關鍵字 雷達估計降雨  即時修正  克利金法  支撐向量回歸 
英文關鍵字 radar precipitation  real-time correction  Kriging  Support Vector Regression 
學科別分類
中文摘要 本研究嘗試利用地面降雨觀測資料,以克利金和支撐向量回歸模式即時修正雷達估計降雨,希望能夠保有雷達估計降雨的高空間解析度並提高雷達估計降雨之準確性,以提供較佳的降雨資訊做為水文上的應用。本研究選取2005~2009共13場颱風事件做為研究資料,從修正結果可以看出,經過克利金修正後,雷達估計降雨的相關係數由原本的0.6提高至0.9,均方根誤差則由8.5下降至0.7,顯示克利金能夠掌握當時的降雨空間資訊並有效修正雷達估計降雨。而支撐向量回歸模式修正雷達估計降雨在率定的部分由原始的0.6提升至0.75均方根誤差由8下降至6,修正效果沒有相當顯著,又從地面雨量站和雷達估計降雨之誤差與高程、雷達距離的分析可以看出,在大甲溪流域,雷達估計降雨的高低估與高程、雷達距離的關係並不密切,故在建立支撐向量回歸模式若只單用這些因子,無法有效掌握颱風特性,造成無法有效修正雷達估計降雨。最後也利用支撐向量回歸修正雷達估計降雨後再進行克利金修正,發現其修正效果也極佳,顯示克利金都能夠有效修正雷達估計降雨。
英文摘要 This study aimed to correct the error of the radar precipitation by using the Kriging and Support Vector Regression (SVR) methods. Precipitation is the key for hydrological researches. Although the raingauge observations can provide accurate precipitation, the sparse spatial resolution of the raingauge network cannot represent the actual precipitation in the ungauged area. The development of radar precipitation has brought the convenience for people to have a better understanding of the spatial distribution of the precipitation. However, the radar precipitation in Taiwan showed very low accuracy while compared with the raingauge data. Thus, the real-time correction of the radar precipitation is necessary before further application. The Kriging and SVR methods were used to correct the radar precipitation to fit the raingauge observation.
The Dajia River basin was chosen as the study area. The radar precipitation and raingauge data of 13 typhoons during 2005-2009 were collected. Both Kriging and SVR methods have the ability to improve the radar precipitation. According to the performance index, the Kriging method has higher correlation coefficient then SVR does, since the factors (elevation and distance to radar) selected by the SVR method have no relationship to the error of the raingauge and radar precipitation in Dajia River. Furthermore, the Kriging method was used to correct the adjusted radar precipitation from SVR method and displayed much better performance than SVR method. Thus, the Kriging method has the best performance in correcting the radar precipitation in Dajia River.
論文目次 中文摘要i
英文摘要iii
誌謝v
目錄vii
圖目錄ix
表目錄xi
第一章 緒論1
1.1 研究動機1
1.2 文獻回顧1
1.3 本文組織架構6
第二章 研究區域與資料介紹9
2.1 研究區域9
2.2 研究資料10
2.3 雷達估計降雨14
2.4 雨量資料整合21
第三章 研究方法23
3.1 克利金推估法23
3.1.1 區域變數理論24
3.1.2 半變異元(semivariogram)分析25
3.1.3 克利金推估法27
3.1.4 克利金法修正雷達估計降雨28
3.2 支撐向量回歸31
3.2.1 支撐向量回歸理論介紹31
3.2.2 支撐向量回歸(SVR)率定參數37
3.2.3 支撐向量機回歸修正雷達估計降雨37
3.3 評鑑指標39
第四章 雷達估計降雨修正結果與討論41
4.1 克利金修正結果41
4.1.1 克利金率定結果41
4.1.2 克利金驗證結果48
4.2 SVR模式修正結果53
4.2.1 SVR率定與驗證結果54
4.2.2 SVR測試結果57
4.3 克利金與SVR修正比較60
4.4 雨量資料分析66
第五章 結論與建議81
5.1 結論 81
5.2 建議 83
文獻回顧85
參考文獻 1.李清勝、周仲島、劉格非、何興亞,2004,土石流潛勢區之雨量估計與即時預報技術發展先期研究,九十三年度農委會水土保持局科技計畫。
2.林淑惠,2010,以超曲面迴歸克利金進行降雨量空間推估桃園,國立中央大學應用地質研究所,碩士論文。
3.張永欣,2007,以多變量地質統計方法進行雨量空間內插,國立中央大學應用地質研究所,碩士論文。
4.張保亮、邱台光、陳嘉榮與林品芳,2006,中央氣象局QPESUMS系統之近期發展介紹,海峽兩岸災變天氣分析與與預報。
5.張智昌,2006,整合氣象雷達與即時降雨資料於颱風降雨推估之研究,國立台灣大學地理環境資源研究所,碩士論文。
6.張逸凡,2005,支撐向量機在即時河川水位預報之應用,國立成功大學水利及海洋工程研究所,碩士論文。
7.陳憲宗,2006,支撐向量機及模糊推理模式應用於洪水水位之即時機率預報,國立成功大學,水利及海洋工程研究所,博士論文。
8.陳憲宗、張逸凡、謝章廷、游保杉,2006,「支撐向量機制:洪水水位預報模式」,台灣水利,54(2):50-61。
9.陳憲宗、游保杉,2007,「洪水水位之即時機率預報-結合支撐向量機與模糊推理模式」,農業工程學報,53(4):1-20。
10.楊政潭,2003,雷達回波應用於颱風降雨空間分佈與總量之研究-以納莉颱風為例,國立中央大學水文科學研究所,碩士論文。
11.劉鑌鋈,2009,利用機器學習修正QPESUMS雷達估計降雨,國立成功大學水利及海洋工程研究所,碩士論文。
12.Atlas, D. & Ulbrich, C. W., 1977, "Path- and area integrate rainfall measurement by microwave attenuation in 1-3 cm band." Journal of Applied Meteorology, 16, 1322-1331.
13.Battan, L. J., 1973, "Radar observation of the atmosphere." University of Chicago Press in Chicago , 324.
14.Berenguer, M. & Zawadzki, I., 2008, "A study of the error covariance matrixof radar rainfall estimates in stratiform rain." Weather and Forecasting 23: 1085-1101.
15.Berne, A., Delrieu, G., Creutin, J. D. & Obled, C., 2004, "Temporal and spatial resolution of rainfall measurements required for urban hydrology." Journal of Hydrology 299: 166-179.
16.Borga, M., 2002, "Accuracy of radar rainfall estimates for streamflow simulation." Journal of Hydrology 267: 26-39.
17.Bray, M. & Han, D., 2004, "Identification of support vector machines for runoff modeling." Journal of Hydroinformatics, 6: 265-280.
18.Cannas, B., Fanni, A., Montisci, A. & See, L., 2004, "Comparing artificial neural networks and support vector machines for modelling rainfall-runoff." 6th International Conference on Hydroinformatics - Singapore, June 21-24.
19.Chandrasekar, V. & Bringi, V. N., 1988, "Error structure of multiparameter radar and surface measurements of rainfall. Part II: Differential reflectivity." Journal of Atmospheric and Oceanic Technology, 5: 783-795.
20.Chang, C. C. & Lin, C. J., 2004, " LIBSVM: a library for support vector machines."
21.Cherkassky, V. & Ma, Y., 2004, "Practical selection of SVM parameters and noise estimation for SVM regression." Neural Networks, 17:113-126.
22.Cluckie, I. D., Griffith, R. J., Lane, A. & Tilford, K. A., 2000, "Radar hydrometeorology using a vertically pointing radar." Hydrology and Earth System Sciences 4(4): 565-580.
23.Cole, S. J. & Moore, R. J., 2008, "Hydrological modeling using raingauge- and radar-based estimators of areal rainfall." Journal of Hydrology 358: 159-181.
24.Cortes, C. & Vapnik, V., 1995, "Support vector networks." Machine Learning 20: 273-297.
25.Fletcher, P. J., 1987, "8-OH DPAT elicits gnawing, and eating of solid but not liquid foods." Psychopharmacology 92:192-195.
26.Germann, U., Galli, G., Boscacci, M. & Bolliger, M., 2006, "Radar precipitation measurement in a mountainous region." Journal of the Royal Meteorological Society 132: 1669-1692.
27.Gerstner, E. M. & Heinemann, G., 2008, "Real-time areal precipitation determination from radar by means of statistical objective analysis." Journal of Hydrology 352: 296-308.
28.Hinterding, A., 2003, "Entwicklung hybrider Interpolations verfahrenf¨ur den automatisierten Betrieb am Beispiel meteorologischerGr¨oßen." Institut für Geoinformatik, Westf¨alische Wilhelms-Universit¨at M¨unster, IfGIprints, 19, M¨unster, Germany (in German).
29.Hong, W. C., 2008, "Rainfall forecasting by technological machine learning models." Applied Mathematics and Computation 200: 41-57.
30.Hsu, C. W., Chang, C. C. & Lin, C. J., 2003, "A practical guide to support vector classification." Technical report, Department of Computer Science, National Taiwan University.
31.Jordan, P. W., Seed, A. W. & Weinmann, P. E., 2003, "A Stochastic Model of Radar Measurement Errors in Rainfall Accumulations at Catchment Scale." American Meteorological Society 4: 841-855.
32.Liong, S. Y. & Sivapragasam, C., 2002, "Flood Stage Forecasting with Support Vector Machine." Journal of the American Water Resources Association 38: 173-185.
33.Liu, H., V. Chandrasekar & Xu, G., 2001, "An adaptive neural network scheme for rainfall estimation from WSR-88D observations." Journal of Applied Meteorology 40(11): 2038-2050.
34.Marshall, J. S. & Palmer, W. M., 1948, "The distribution of raindrops with size." Journal of Meteorology 5: 165-166.
35.Paulat, M., Frei, C., Hagen, M., & Wernli, H., 2008, "A gridded dataset of hourly precipitation in Germany: Its construction, climatology and application . " Meteorologische Zeitschrift 17(6): 719-732.
36.Pluntke, T., Jatho, N., Kurbjuhn, C., Dietrich, J. & Bernhofer, C., 2010, "Use of past precipitation data for regionalisation of hourly rainfall in the low mountain ranges of Saxony, Germany." Natural Hazards and Earth System Science 10(2):353-370.
37.Rosenfeld, D., Wolff, D. B. & Amitai, E., 1994, "The window probability matching method for rainfall measurements with radar." Journal of Applied Meteorology 33: 682-693.
38.Sivapragasam, C., Lion, S. Y. & Pasha, M. F. K., 2001, "Rainfall and runoff forecasting with SSA-SVM approach." Journal of Hydroinformatics 03.3: 141-151.
39.Steiner, M., & Smith, J. A., 2002, "Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoesin radar data. " Journal of Atmospheric and Oceanic Technology 19: 673-686.
40.Sun, X., Mein, R. G., Keenan, T. D. & Elliott, J. F., 2000, "Flood estimationusing radar and raingauge data." Journal of Hydrology 239(1-4): 4-48.
41.Teschl, R., Randeu, W. L. & Teschl, F., 2007, "Improving weather radar estimates of rainfall using feed-forward neural networks." Neural Networks 20: 519-527.
42.Umehara, S., Yamazaki, T. & Sugai, Y., 2006, "A Precipitation Estimation System Based on Support Vector Machine and Neural Network." Electronics and Communications in Japan, Part 3 89(3) (Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J86-D-II, No. 7, July 2003, pp. 1090-1098): 38-47.
43.Vojinovic, Z. & Kecman, V., 2004, "Contaminant Transport Modelling with Support Vector Machine Model: An Alternative to Classical Advection-Dispersion Equation." 6th International Conference on Hydroinformatics - Singapore, June 21-24.
44.Wexler, R. & Atlas, D., 1963, "Radar Reflectivity and Attenuation of Rain."Journal of Applied Meteorology, 2(2):276-280.
45.Xin, L., Recuter, G. & Larochelle, B., 1997, "Reflectivity-rain rate relation -ship for convective rainshowers in Edmonton." Atmosphere Ocean 35: 513 -521.
46.Yu, P. S., Chen, S. T. & Chang, I. F., 2005, "Real-time flood stage forecasting using support vector regression." European Geosciences Union (EGU) General Assembly 2005, Vienna, Austria, 24-29.
47.Yu, P. S., Chen, S. T. & Chang, I. F., 2006, "Support vector regression for real-time flood stage forecasting." Journal of Hydrology 328(3-4): 704-716.
48.Yu, X. & Liong, S. Y., 2004, "Forecasting of Chaotic Hydrological Time Series with Ridge Linear Regression in Feature Space." Proceedings of the 6th International Conference on Hydroinformatics, S. Y. Liong, K. K. Phoon, and V. Babovic, eds., World Scientific Publishing Co., Singapore.
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