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標題: 以類神經網路預測淡水河口暴潮偏差之研究
A Study on Storm-Surge Prediction at Tanshui Estuary by Artificial Neural Network
作者: 游智宇
You, Chih-Yu
關鍵字: Storm-Surge
Multi Layer Perception Network
Radial Basis Function Network
出版社: 土木工程學系所
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摘要: 目前台灣淡水河流域具有一些不利於防範洪水與暴潮之條件,如盆地低窪、過去遭遇累積地層下陷等之影響,加上颱風每年侵襲台灣北部頻繁常有洪患,由於颱風暴潮偏差其影響因素較為複雜,不容易以數值方法或經驗公式精確地預測,本文利用具有監督網路收斂最佳化優點的多層感知器類神經網路及即時演算的徑向基底函數類神經網路,建立颱風暴潮偏差之預測模式,包括最大暴潮偏差之預測及暴潮偏差時序列之預測。 最大暴潮偏差預測模式中首先以前人所提出最大暴潮偏差之經驗公式進行回歸得出相關係數僅為0.565。本文利用兩種不同類神經模式以22組颱風資料進行模擬並探討其各項因子對暴潮偏差預測之影響,最後結果以輸入測站最大氣壓差、風速即可獲得極佳的淡水河口最大暴潮偏差值預測且相關係數皆達0.9以上。 暴潮偏差時序列之預測模式在輸入即時測站氣壓差、風速及風向及前一時刻之暴潮偏差之情況下,驗證組及測試組之相關係數皆為0.85以上,結果顯示可證實時序列模式對於颱風暴潮偏差時序列的預測可達到良好的預測成果。
Taiwan northern area is always attacked by typhoon frequently every year and induces the flood disasters. At present, Tamsui river territory has some unfavorable conditions including the basin low-lying and land subsidence to control the flood with storm-surge. Thus the accurate prediction of the storm-surge is an important issue for the area. However, it is quite complex for the prediction of storm-surge and use the numerical method or empirical formula to predict the phenomenon is not easily. Alternatively This paper applies the artificial networks including the supervised multilayer perception neural network and the radial basis function neural network, for the prediction of the storm-surge . Based on the previous empirical formula of the maximum of storm-surge, it is only 0.565 to draw the correlation coefficient. This study chooses the stand atmosphere pressure variation, wind speed and wind direction parameters as the input neurons for the networks of typhoon about 22 groups and discuss the effect of each parameter on storm-surge forecast. The results agree well with the measured data of storm-surge, which all the correlation coefficient are more than 0.9. The results of the predicted and test model show that the correlation coefficient values are larger than 0.85 in the situation of predicted model inputted the atmosphere pressure variation, wind speed, wind direction and storm-surge of last moment parameters into the time series of storm-surge. This result illustrates that time series model forecast well for the storm-surge of the time during the typhoon.
其他識別: U0005-2708200722053300
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