本研究旨在應用兩種機器學習法－支撐向量機與隨機森林建置資料驅動模式，以發展一可靠之颱風時雨量預報，並引入非支配排序遺傳演算法結合兩學習法，分別建立變量優選模式以決定最適變量組，最後比較兩學習法於颱風時雨量預報之表現。首先，本研究蒐集雨量因子、颱風特性因子以及氣象因子，以變量優選模式優選四種因子組合：(1)因子組合一：雨量因子(2)因子組合二：雨量因子與颱風特性因子(3)因子組合三：雨量因子與氣象因子(4)因子組合四：雨量因子、颱風特性因子及氣象因子，探討各組合之最佳變量組對於颱風時雨量預報之表現。兩學習法於四種因子組合之測試結果顯示：因子組合四考量之輸入變量種類最為全面，以其優選變量建置之颱風時雨量預報模式有最好之表現。此外，本研究選用氣象站資料與自記式雨量站資料，分別採兩機器學習法搭配因子組合四之最佳變量組建置颱風時雨量預報模式，於輪流驗證中兩評鑑指標結果顯示：以平均絕對誤差來看，支撐向量機於時雨量預報之表現略優於隨機森林，若以平均效率係數來看，隨機森林於時雨量預報之表現則優於支撐向量機。於測試場次之時雨量預報結果顯示：支撐向量機與隨機森林於短前置時間之雨量預報表現相當，但於長前置時間之雨量預報則以隨機森林表現較佳。最後，本研究針對集水區上游自記式雨量站建立之颱風時雨量預報模式結果顯示：運用自記式雨量站附近之氣象資訊可有效改善颱風時雨量預報準確性，預報模式能提供可靠預報雨量資訊，以利防災單位進行洪水預警或防洪操作。

This study aims to develop and compare the rainfall forecasting models based on two machine learning (ML) methods, support vector machines (SVMs) and random forests (RFs). Furthermore, an optimization algorithm named non-dominated sorting genetic algorithm (NSGA-II) is applied to construct a optimizing program for searching the optimal predictor set to improve the performance of forecasting. Firstly, four predictor sets: (1) antecedent rainfalls, (2) antecedent rainfalls and typhoon characteristics, (3) antecedent rainfalls and meteorological factors, and (4) antecedent rainfalls, typhoon characteristics and meteorological factors, respectively, were optimized by using NSGA-II to construct 1- to 6-hour ahead rainfall forecasting and the model performances were also investigated. The results reveal that the ML-based models with predictor set #4 as inputs show a significant improvement when compared to predictor set #1 especially for the long lead time forecasting. Finally, the performances of the SVM-based and RF-based model using the optimal predictors from set #4 were further compared. It is found that the RF-based model is superior to the SVM-based model in hourly typhoon rainfall forecasting.

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