異常波浪會發生在岸邊與海上，因突然出現對岸上的遊客或海上操船或工作平台是一大威脅。為減少人員生命財產損失，發展機率預警系統有其必要。海上與近岸的異常波浪發生機制不同，近年來有許多研究探討海洋異常波浪演化的過程，但仍舊不完全了解；在近岸異常波浪(瘋狗浪)的研究，僅有少數國內學者討論。因發生機制複雜且尚未完全了解，本研究仰賴統計的方法進行預警模式建置，旨在發展海洋異常波浪與海岸瘋狗浪發生機率預測的方法，並進一步建置預警模式。在海洋異常波浪部分，彙整前人研究成果，得到一套異常波浪發生機率估算理論，然該理論採用線性波假設，本研究對估算理論進行修改，並以台灣海域的波浪資料進行率定，得到適合台灣海域的異常波浪發生機率估算理論。估算理論可結合業化波浪模式預報資料，完成一套異常波浪預報模式。預警模式以東吉島連續觀測之波浪資料進行驗證，24小時、36小時和48小時的異常波浪預測機率誤差分別為15.9%、22.9%和20.1%，顯示預警時間越長，預報的誤差越大，但整體來說預警模式具可參考之價值。本研究分析預警模式運作結果，發現當有天氣系統生成時，天氣系統的邊界通常有較高的異常波浪發生機率，且當鋒面通過台灣海峽時，也有異常波浪顯著提升的現象。本研究也分析數個颱風範圍內的異常波浪發生機率，顯示在颱風的第1象限有較高的發生機率，第三象限的發生機率較低，此結果與前人的研究結果相符，顯示異常波浪預警模式預測結果相當合理且可靠。
本研究使用SPH(Smooth Particle Hydrodynamic)粒子法模擬2012年基隆海洋大學外一件瘋狗浪案例，探討波浪越堤後水粒子的運動行為，除了對越堤浪花特性有所掌握，亦發現堤前消波塊會使越堤水量減少，但增加水粒子的水平穿越速度。在瘋狗浪預警模式的部分，本研究蒐集台灣東北角海域的近岸影像，並發展了一套瘋狗浪影像分析程序，從影像中以二值法與均值法萃取浪花邊界，並從浪花的高度與浪花速度定義瘋狗浪，經人工驗證，該方法辨識準確，成功辨識率達86%。本研究在瘋狗浪預警模式建置採用類神經網路方法，以過往瘋狗浪落海事件與浮標資料作為輸入資料與訓練之樣本，輸入資料包含7個與瘋狗浪生成有關之海氣象因子，預警模式可預測未來12、18和24小時之瘋狗浪發生機率。本研究以瘋狗浪落海事件與瘋狗浪影像案例進行驗證，兩者正確率約有8成；同時也參考氣象局長浪即時訊息進行驗證，結果顯示預警模式可以提前發布預警。
本研究建置的兩套異常波浪與瘋狗浪模式，本研究也結合波浪模式進行瘋狗浪與異常波浪機率預測，經驗證兩模式可準確預測發生機率，顯示兩模式已可實際應用。

Freak waves may be generated and occur on the coast and in the ocean. The rapidly appearing freak wave is a huge threat for the people on the coast and on ships. It is necessary to develop a freak wave warning model to reduce the loss of life and property. The mechanics of freak wave generation in the ocean and coast are different. In recent years, many studies have explored the evolution of oceanic freak waves (OFWs), but it is still not fully understood. Only a few researchers have discussed coastal freak waves (CFWs). The mechanics of coastal freak waves are also not fully analytical. Warning models need to rely on stochastic methods for development.
The purpose of this study was to propose a method to predict the occurrence probability of oceanic and coastal freak waves. A warning model was constructed by combining the operated wave model and the prediction method. The probability estimation formula for oceanic freak waves was obtained from literature. However, the formula was derived based on the linear wave assumption, in which there is an error under highly nonlinear situations. The new formula to estimate the probability of oceanic freak waves was proposed and calibrated based on the wave data observed in Taiwan waters. The verification of the theory shows that the error has changed from 22.3% to 13.2%. In this study, the operational wave model was applied as the input of the warning model to forecast the occurrence probability of oceanic freak waves. Coupled with the wave model operation, the occurrence probability of OFW could be calculated from the nonlinear parameter (μ4) of the wave, which can be obtained from the wave model output. The warning time of the model was 24, 36, and 48 hours. Verification result of the warning model was obtained by continuous wave observation on Dongji Island. The prediction probability errors of OFW at 24 hours, 36 hours and 48 hours were 15.9%, 22.9% and 20.1%, respectively. The forecast error was greater for longer warning times. The warning model has been tested and operated since 2016. The model output show that when a weather system was generated, the location close to the edge of the weather system usually had a higher probability of OFW. The prediction results of the warning model are quite reasonable and reliable.
Near-shore optical images in northeastern Taiwan were collected and analyzed in this study. A coastal freak wave image analysis procedure was also proposed. The coastal freak wave was identified through the splash height and speed. The boundary of the splash was extracted from the image by edge detection technology with a success rate of 86%. The procedure was accurate in identifying CFWs. SPH (Smooth Particle Hydrodynamic) model was applied to simulate a CFW event at National Taiwan Ocean University in Keelung in 2012. The hydrodynamic behavior of the wave overtopping was discussed. The results showed that the armor block in front of the breakwater will reduce the discharge of wave overtopping and increases the horizontal throw speed. In the warning model, a neural network algorithm was used in the construction. The CFW events and buoy data were used as training samples and input data. The model predicts the occurrence probability of a CFW with lead times of 12, 18 and 24 hours. This research was verified by CFW events from media and from optical video. The accuracy rate of the two was approximately 80%. The warning model was also verified by the swell warning announced by CWB. All the verification results showed that the model can provide reliable warning information.
CFW and OFW model proposed in this study operated with combination of wave model. The outputs of occurrence probability of CFW and OFW are accurate which mean the model could be used in practical.

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