臺灣博碩士論文加值系統

颱風波浪主要是由颱風內複雜風場所產生，為了掌握颱風波浪的變化情形必須先了解颱風內部風場分佈情況，所以研究中針對影響颱風波浪之颱風運動軌跡、風場及規模變動參數進行一系列討論。本文延續蘇(2003)應用倒傳遞類神經網路建立一颱風波浪波高及週期的預報模式，研究中決定採用的輸入參數為：颱風最大暴風半徑處海面上10公尺之風速、颱風移動速度、颱風中心與觀測站間的距離、颱風之方位角、颱風中心移動方向與到測站連線的夾角以及測站實測波浪資料，並且利用中央氣象局及港灣技術研究中心兩花蓮測站波浪資料進行實例探討，根據討論結果顯示本文所建構之颱風波浪預報模式有效地改進蘇(2003)的模式，並且可以合理地預測颱風波浪波高及週期。此外，為了提高模式的實用性，本研究嘗試結合湧浪模式，以模式計算所得之波高值代替測站之實測值，做為預報模式之輸入參數；另亦嘗試不輸入測站之實測波浪資料逕行預報，但結果均無法達到預期理想。

The typhoon waves are mainly generated by the intricate wind field of typhoon. In order to realize the variation of typhoon waves, the distribution of wind field of typhoon has to be understanded. Therefore, the influential factors of typhoon waves such as propagating path of typhoon, the variation of wind field and the scale of typhoon are discussed in this study. This paper extended the study of Su (2003) which applied a back-propagation neural network method to predict typhoon wave height and period in a station. In this study, maximum sustained wind speed at 10 m (33 ft) above the mean water level, forward speed of the typhoon, the distance from the center of typhoon to the station, the azimuth of typhoon, the angles between moving wind direction and the hurricane center and the historical wave data in the station were taken as the input parameters of the typhoon waves prediction model. Those wave data on Hwa-Lien wave station measured by the Central Weather Bureau and the Harbor and Marine Technology Center were used for practical typhoon waves prediction. From the results of this study, it indicates that the present model has effectively improved Su’s (2003) model and also has a rationalize on typhoon wave height and period.

目 錄
摘 要 I
Abstract II
目 錄 III
表 目 錄 VI
圖 目 錄 VII
符 號 說 明 XIII
第一章 前言 1
1-1 研究動機與目的 1
1-2 相關文獻回顧 2
1-2-1 波浪推算模式 3
1-2-2 颱風波浪推算模式 4
1-2-3 類神經網路在海洋工程之應用 5
1-3 本文架構 8
第二章 類神經網路理論 9
2-1 類神經網路簡介 9
2-2 類神經網路發展史 9
2-3 類神經網路之分類 11
2-4 倒傳遞類神經網路 13
2-4-1 倒傳遞類神經網路之特性 13
2-4-2 倒傳遞類神經網路架構 14
2-4-3 倒傳遞網路演算法 15
第三章 颱風波浪預報模式參數之探討 23
3-1 颱風相關資料收集 23
3-1-1 範例蒐集與處理 23
3-1-2 網路評鑑效能 24
3-2 影響颱風波浪之參數特性探討 25
3-2-1 颱風風場特性之探討 27
3-2-2 颱風波浪預報模式之輸入參數 30
3-2-3 颱風氣象及波浪資料之相關性探討 33
3-3 颱風波浪預報模式之改良 34
3-3-1 網路架構探討 34
3-3-2 網路參數探討 36
3-3-3 最佳網路架構及參數選定 37
第四章 颱風波浪預報實例探討 49
4-1 花蓮測站颱風波浪預報 49
4-1-1 中央氣象局花蓮測站颱風波浪預報 49
4-1-2 港研中心花蓮測站颱風波浪預報 52
4-1-3 結合兩花蓮測站資料進行颱風波浪預報 54
4-2 簡化輸入參數之探討 56
4-2-1 輸入參數簡化實測颱風波浪資料 56
4-2-2 結合湧浪模式 58
第五章 結論與建議 94
5-1 結論 94
5-2 建議 95
參 考 文 獻 97
謝 誌 102
作 者 簡 歷 103

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