台灣四面環海，海岸遊憩活動興盛，海岸瘋狗浪(coastal freak wave)常將海岸邊的遊客或是釣客捲入海中，其發生機制複雜至今仍未有理論可以完整說明，很難預測發生的時機和地點。近年因電腦計算能力增強，因此本研究利用人工智慧(AI)領域中能有效處理非線性、小樣本資料的支撐向量機(support vector machine, SVM)方法來建置瘋狗浪機率預警系統，並探討影響瘋狗浪發生的因子。
本研究根據現場光學監視站監測所得的瘋狗浪事件以及鄰近浮標數據做為分析資料，使用支撐向量機方法並採取網格搜尋法擬定訓練參數建置瘋狗浪預測模型，在瘋狗浪資料中找出最佳的分類依據並能對新的資料進行預測，驗證結果顯示模型預測效果良好，模型預測的正確率達75%。本研究透過探討不同因子對模式的影響程度，發現選取的13個潛在因子之間具有相關性，研究結果證實，使用全部因子來建置瘋狗浪預測模式能得到最佳的結果，其中，輸入因子中以方向類別因子(包含風向、波向)對結果的影響最顯著，其中以波向影響最大，代表波向是造成海岸瘋狗浪發生的重要關鍵。本研究也發現，當波浪週期較長、湧浪波高較高以及波向與風向方向較一致時，瘋狗浪有較高發生機率。
本文與前人以類神經網路建置之瘋狗浪預測模式結果進行比較，發現類神經網路方法在實際有瘋狗浪發生時的預測能力較佳，而支撐向量機無論實際瘋狗浪是否發生均能有良好的預測能力，顯示透過這類人工智慧技術均可合理預測海岸瘋狗浪發生機率。

Taiwan is surrounded by the sea, so and coastal recreational activities are thriving. Coastal freak waves often attack tourists or fisherman from the coast into the sea. The mechanism of coastal freak wave is complicated. But there is lack of theory that can fully explain its occurrence, and it is difficult to predict when and where will happen. Due to the increase in computer computing ability in recent years, this study uses the support vector machine (SVM) method in the field of artificial intelligence (AI), which can effectively process nonlinear and small quantity sample data, to build a coastal freak waves probability warning system and discusses the factors which affecting the occurrence of freak waves. In this study, the incidents of coastal freak wave are monitored by the on-site optical monitoring station and the adjacent buoy data was used. In training section, grid search method was applied to draw up training parameters to build a prediction model of coastal freak wave occurrence by SVM. Overall, the verification results show that the model has good predictive effect and accuracy rate that up to 75%. This study explores the degree of influence of different factors on the model and found that the selected 13 potential factors that affect the occurrence of coastal freak wave are correlated. The results of the study confirm that using all the factors to build the prediction model of freak wave can get the best results. Among the input factors, the direction category factors (including wind direction and wave direction) have the most significant influence on the results, and the wave direction has the greatest influence, which represents the wave direction is the important key to the occurrence of coastal freak wave. This study compares the results of the prediction model of freak wave that built by the artificial neural network (ANN), and finds that the ANN has the better predictive ability when the freak wave occurs, and the SVM predicts well whether freak wave occurs or not. The good predictive capabilities show that through this type of AI technology, the probability of occurrence of coastal freak wave can be reasonably predicted.

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