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 波浪為海面上最主要天然作用力之一，台灣四周環海，波浪對人們的海上活動影響甚巨，不論是在航行運輸、海岸工程、休閒活動、防災救難及海上軍事操演等，都會受到波浪作用所左右，若能對海面的波高作出合理的預測，則對人們從事海上活動將有莫大助益。 本研究分別以大鵬灣及小琉球氣象浮標水文資料來建立波高預測模式，輸入變數是以浮標本身觀測波高為主體，再配合可能影響波高之其他相關水文資料為輸入變數，如週期、風速及風向等，利用GMDH(Group Method of Data Handling)理論所特有之自組多層演算方法，建立輸入∼輸出關係之「波高預測模式」進行波高預測。首先係以試誤法(Trial and Error)決定出最佳的建模筆數及輸入變數，並以逐步迴歸之GMDH(SGMDH)來改善GMDH演算法會衍生高階非線性項而降低其實用性的缺點，再經由模式之先期模擬檢定作稽延時間之修正，即能架構最佳之「波高預測模式」。另本研究嘗試將即時觀測的新資料再納入模式中重新建模，利用遞迴結構對模式參數作即時修正，使模式具時變性而能自我調整，以達長期觀測、精確預測的目標。 應用本文模式推算結果的波高變化趨勢與浮標實測值一致，由各季觀測值與預測值之誤差比較，得知大鵬灣預測結果最大誤差21.66 ~3.89 cm，最小誤差0.19 ~ 0.01 cm，平均誤差僅4.96 ~ 1.22cm；小琉球預測結果最大誤差12.46 ~ 1.23cm，最小誤差0.10 ~ 0.01cm，平均誤差亦僅3.13 ~ 0.74cm。故本文所建立的「波高預測模式」符合要求，可實際應用於海面波浪波高之預測推算。
 Wave is one of the natural forces and will affect human’s activities in the ocean such as sailing, leisurely movements, offshore engineering, disaster rescuing and military training. Reasonable accuracy wave forecasting data should do great help for all kind of marine activities. The hydrological data of wave height as well as period, wind speed and direction collected by the data buoy in Dapeng Bay and Little-Liouciou are used to be input variables to set up the GMDH wave height forecasting model. Trail and error procedure provided first to determine input variables and sums for the best model fitting. The SGMDH process then introduced to improve the ill condition of high rank nonlinear terms which is derived through the self-organizing multilayer algorithm of GMDH structure. The optimum wave height forecasting model could be constructed after the calibrate step to revised the time lag between simulated and measured results. In addition, model parameters could be modified by the stepwise regression algorithm using the update data and threshold to achieve the purpose of self-regulating, long term and time variable wave height accurate forecasting. The trend of forecasting wave height series were in agreement with measuring materials of the data buoy in this research. The maximum and minimum error between forecasting and measuring data of Dapeng Bay data buoy are 21.66~3.89cm and 0.19~0.01cm respectively and have an average error of 4.96~1.22cm. As to Little-Liouciou data buoy, its maximum and minimum error are 12.46~1.23cm and 0.10~0.01cm respectively and get an average error of 3.13~0.74cm. So, the GMDH forecasting model proposed by this paper is in conformity with the requirement of forecasting and could be applied in practical usage for wave height prediction at the specified location.
 中文摘要····················································Ⅰ英文摘要····················································Ⅱ謝 誌·······················································Ⅲ目 錄·······················································Ⅳ表目錄······················································Ⅶ圖目錄······················································Ⅷ照片目錄····················································Ⅹ第一章 緒 論·················································1 1-1 研究動機···············································1 1-2 研究方法···············································3 1-3 相關文獻回顧···········································4 1-4 本文組織···············································7第二章 GMDH理論 ·············································8 2-1 基本架構···············································8 2-2 基本理論···············································8 2-2-1 GMDH演算法···········································9 2-2-2 逐步迴歸之GMDH演算(SGMDH)···························14 2-2-3 遞迴結構之GMDH演算··································17第三章 波浪預測模式之建立與驗證 ····························21 3-1 波浪預測在海上航行安全之重要性 ·······················21 3-2 波浪預測模式之建立 ···································26 3-3 波浪預測效能評鑑指標 ·································28 3-4 波浪預測模式之驗證(實例驗證) ·························29 3-4-1 大鵬灣及小琉球附近海域地形及氣海象概述 ············29 3-4-1-1 沿海海底地形 ···································29 3-4-1-2 氣海象狀況 ·····································30 3-4-2 資料概述············································31 3-4-3 資料浮標系統········································32 3-4-4 波浪預測模式參數之選定······························35 3-4-5 波浪預測模式之建立與預測····························35 3-4-6 資料更新重新建模之預測······························39第四章 分析結果與討論 ······································40 4-1 GMDH及SGMDH之模擬結果與比較 ··························40 4-2 最佳輸入變數組合 ·····································41 4-3 波浪預測結果分析 ·····································42 4-4 以新數據重新建模之預測結果比較 ·······················56 4-5 波高預測應用探討 ·····································59 4-5-1 波浪變形原理 ·······································59 4-5-2 折射圖繪製方法 ·····································63 4-5-3 計算結果 ···········································68第五章 結論與建議 ··········································74 5-1 結 論 ················································74 5-2 建 議 ················································75參考文獻 ···················································77
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 1 自組非線性系統應用於濁度預測 2 自組非線性系統應用於水位相關之研究 3 自組非線性系統應用於水位即時預測 4 自組非線性系統應用於地層下陷之預測 5 自組非線性理論建立水位預測模式之探討 6 自組非線性系統應用於水庫優養化之預測 7 近岸風浪推算資料同化之研究 8 海洋波譜特性及其於分析波浪之應用 9 資料處理群集分析演算法應用於颱風移動路徑預測模式之探討 10 自組非線性系統應用於颱風降雨之預測-以三地門雨量站為例 11 海域鄰近測站流速及流向之補遺及預測 12 自組非線性系統應用於颱風波高預測 13 自組式網絡架構近岸海域颱風波高預測模式及數據補遺方式探討

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