Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/16305
標題: 類神經網路模式應用於颱風暴潮時序列預測
Apply Neural Network Model for Storm-Surge Time-series Predition
作者: 陳宣志
Chen, Hsuan-Chih
關鍵字: Neural Network
類神經網路
Storm-Surge
Time-series
颱風暴潮
時序列
出版社: 土木工程學系所
引用: 1.Chang, F. J. and Chen, Y. C. (2003)“Estuary water-stage forcasting by using radial basis function neural network,”Journal of Hydrology, Vol. 207, pp. 158-166. 2.Deo﹐M. C. and Sridhar, Naidu, C. (1999)“Real time wave forecasting using neural network,”Ocean Engineering, Vol. 26, pp. 191-203. 3.Fan,C.W. and Kao, S. J. (2008)“Effects of climate events driven hydrodynamics on dissolved oxygen in a subtropical deep reservoir in Taiwan,”Science of The Total Environment 393, pp. 326-332. 4.Grubert,J. P.(1995)“Prediction of estuarine instabilies with artificial neural,”J.Comput,Civ.Eng, Vol. 9 , No. 4, pp. 266-274. 5.Horikawa (1978) Coastal Engineering, Tokyo University Press, pp.153-167. 6.Hopfiled, J. (1982)“Neural network and thysical systems with emergent collective computational properties,” Proceedings of the National Academy of Sciences of the USA, Vol. 79, pp. 2554-2588. 7.Jan, C. D., Yen, p. h., Lee, Y. P. and Lee, H. F. (1995)“Determination of parameters of harmonic tide-level model by using Kalman filter,”第17屆海洋工程研討會暨 1995 兩岸港口及海洋開發研討會論文集,pp. 287-298。 8.Lee, T. L., Tsai C. P., Jeng, D. S. and Hsu, Y. J. (2004)“Tital Level Forecasting during Typhoon Surge using Functional Network,”The ninth conference on artificial intelligenxe and applications. 9.Lee, T.L. (2004) “Back-propagation neural network for long-term tidal predictions,” Ocean Engineering, Vol. 31, pp. 225-238. 10.Lee, T. L. (2006)“Neural Network Prediction of a Storm Surge,”Ocean Engineering, Vol. 30, pp. 85-103. 11.Marzenna, S. (2003)“Forecast of storm surge by means of neural network,”Journal of Sea Research, Vol. 49, pp. 317-322. 12.Serre, S. (2003) “Tidal-level forecasting and filtering by neural network model,” Costal Engineering Journal, Vol. 45, pp.119-137. 13.Tsai, C. P., Lee, T. L. and Chu, L. H. (1999) “Forecasting of wave time series using back propagation neural network,” Journal of the Chinese Institute of Civil and Hydraulic Engineering, Vol. 11, pp. 589-596. 14.Tsai, C. P., Lin, C. and Shen, J. N. (2002) “Neural network for wave forecasting among multi-stations,” Ocean Engineering, Vol. 29, pp.1683-1695. 15.Tsai, C. P., Lee, T. L., Yang, T. J. and Hsu, Y. J. (2005) “Back-propagation neural networks for Prediction of Storm Surge,” Structural and Environmental Engineering, Civil-comp Press. Vol. 11, pp. 589-596. 16.Tsai, C. P. and Chen, H. B. (2006)“Finite volume method for storm-surge simulations,”Proc.The 6th Japan-Taiwan Joint Seminar on Natural Hazard Matigation . 17.朱良瀚 (1997) 倒傳遞類神經網路在波浪預報之應用 ,國立中興大學土木工程研究所碩士論文。 18.葉怡成 (1998) 類神經網路模式應用與實作,儒林圖書有限公司。 19.蔡瀚陞、陳明仁、顏清連 (2000) 「淡水河口歷史颱風暴潮偏差之推估與頻率分析」,第11屆水利工程研討會論文集,第 115-119 頁。 20.馬樹俠 (2000) 結合潮汐理論與類神經網路在潮汐預報上之研究 ,國立交通大學土木工程研究所碩士論文。 21.蔡憶雯 (2001) 淡水河口暴潮位及河川水位機率預報之研究 ,國立台灣大學土木工程學研究所碩士論文。 22.王瀚德 (2001) 小波理論與類神經網路應用於潮汐之預測與補遺 ,國立中山大學海洋環境及工程研究所碩士論文。 23.張憲國 (2002),「應用遺傳演算法於潮汐預報之研究」,第24屆海洋工程研討會論文集,第445-452 頁。 24.李宗霖、蔡清標、謝榮哲、徐月娟、陳進益、劉聖義(2002)「以倒傳遞類神經網路在潮汐補遺之應用」,第24屆海洋工程研討會論文集,第 453-460頁。 25.鄭允祥(2003), 颱風暴潮與颱風特性關係之研究 ,國立成功大學水利及海洋工程研究所碩士論文。 26.李宗霖、S. Rajasekaran、徐月娟、楊宗儒 (2003)「序列學習神經網路在颱風期間之潮位預測」,第25屆海洋工程研討會論文集, 第275-279頁。 27.張憲國、錢維安、何良勝 (2003) 「應用類神經網路在台灣東岸海域颱風波浪之研究」,海洋工程學刊,第三卷,第一期,第73-95頁。 28.張斐章、張麗秋(2005)類神經網路,台灣東華書局,台北。 29.何俊燐 (2008) 以類神經網路預測直立堤波浪越波量之研究 ,國立中興大學土木工程研究所碩士論文。 30.蔡清標、游智宇(2009)「類神經網路在暴潮偏差預測之研究-以淡水河口為例」,第31屆海洋工程研討會論文集, 第133-137頁。
摘要: 颱風暴潮是由氣象條件所引致的異常海水位變化,係由實測海水位扣除天文潮位後的差値即稱為暴潮偏差。建立一套準確而實用的颱風暴潮預測模式,對於海岸防範措施的建立而言,是非常必要的。在過去幾十年以來,已有許多學者以數值模擬或是經驗公式來預測颱風暴潮。本研究利用倒傳遞類神經網路,經由學習與測試淡水河口站的颱風暴潮資料來建立一套良好的預測模式。以t-1時刻之暴潮偏差與當地t時刻之氣象條件(氣壓差、風速與風向)當做模式輸入條件,即可預測t時刻之暴潮偏差。基於氣象局可以提供當地三小時之氣象預報資料,該模式可以有效預測至t+2時刻之暴潮偏差。結果顯示,類神經網路可藉由當地氣象預報資料來預測颱風暴潮。
The storm surge is caused by meteorological factors, which is defined by the difference between the actual observed water level from the tidal gauge and the predicted astronomically-induced tide during the storm. The estimation of the storm surge is essential for the planning of countermeasures against coastal flooding. During the past several decades, the numerical hydrodynamic models or the empirical methods were conventionally used to estimate the storm surge. In this thesis, an application of the artificial neural network (ANN) with the back-propagation procedure for forecasting the storm surge is proposed. Based on the learning and the testing from the data of storm surge observed at Tanshui Estuary station, the ANN model is well established. The results show that the storm surge at time t for a considered station can be predicted using the inputs including the storm surge at time t-1 and the local meteorological conditions (atmosphere pressure deviation, wind speed and wind direction) at time t. Based on the 3-hours forecasting of the local meteorological data provided by Bureau of Meteorology, this model can be extended to forecast satisfactorily the storm surge at time t+2. Results show that the ANN is a feasible technique for the forecast of the storm surge at a station using the forecasting data of local meteorological conditions.
URI: http://hdl.handle.net/11455/16305
其他識別: U0005-2308201018393200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2308201018393200
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