支持向量機於颱洪時期雨量及淹水預報之研究

Typhoon Rainfall and Inundation Forecasting Using Support Vector Machines

鍾秉宸(Bing-Chen Jhong)

指導教授 ： 林國峰

國立臺灣大學/工學院/土木工程學研究所/博士(2015年)

https://doi.org/10.6342/NTU.2015.00164

摘要

颱風侵襲台灣期間，劇烈降雨常導致嚴重之淹水災害，造成人民生命與財產的損失，因此雨量與淹水預報為預警工作中相當重要的一環。為達到此一目標，本研究應用近年來常被使用之類神經網路─支持向量機，建構預報未來一至六小時之雨量與淹水預報模式。然而，傳統上常使用試誤法來建置類神經網路模式，在使用上不但不方便，也相當耗費時間。此外，過去文獻大多使用支持向量機建立點預報淹水模式，鮮少研究應用支持向量機進行探討未來長延時之區域淹水預報。因此，本論文之目的為發展新型之預報模式以改良傳統雨量與淹水預報模式的缺點，以提升模式效能與表現。本論文內容將分成兩部份展現所提出之模式優點，分別描述如下。
於論文第一部份，本研究提出一個新型颱風雨量預報模式以改善時雨量預報準確性。首先，結合多目標基因演算法與支持向量機以得到最佳輸入項組合。再以最佳輸入項組合為基礎，透過各雨量站之雨量預報值並藉由空間內插方法得到降雨過程之空間特徵。本研究以曾文溪集水區作為實際應用，呈現提出模式的優點，並將提出模式預報結果與傳統支持向量機模式作比較。結果顯示本研究所提出之模式比傳統上所使用之試誤法而建立的模式更具優勢，且能有效改善預報表現。
於論文第二部份，本研究提出一個於颱風期間有效之淹水預報模式以產生未來1至6小時之淹水地圖。首先，選取以7-Eleven便利商店為主之網格作為淹水點，接著以支持向量機為基礎以發展點預報模組，產出各淹水點之未來1至6小時淹水預報值。最後，根據點預報結果與地理資訊，使用支持向量機建置空間延展模組，得到未來1至6小時之淹水空間預報值。本研究以台灣嘉義市作為實際應用以呈現提出模式的優點。其應用結果亦顯示，提出模式不僅可準確預報各點淹水深度，且能產出未來1至6小時準確的淹水地圖。綜上所述，本研究所提出之新型預報模式對於雨量及淹水預報有很大的助益。

關鍵字

支持向量機 ； 小時颱風雨量預報 ； 淹水預報 ； 淹水地圖 ； 災害預警系統

並列摘要

Heavy rainfall caused by typhoons frequently result in inundation which frequently leads to loss of human life and property. Typhoon rainfall and inundation forecasting are very important issues in early warning systems. In this thesis, effective rainfall and inundation forecasting models based on the support vector machine (SVM) are proposed. However, the traditional models were established using the trial and error method, which requires much time. Moreover, the conventional SVM-based models are used to produce point forecasts rather than regional forecasts. In this thesis, effective approaches are established to construct forecasting models in rainfall and inundation forecasting. Two parts are conducted herein to demonstrate the superiority of the proposed models.
In the first part of the thesis, a typhoon rainfall forecasting model is proposed to yield 1- to 6-h ahead forecasts of hourly rainfall. First, an input optimization step integrating multi-objective genetic algorithm with SVM is developed to identify the optimal input combinations. Second, based on the forecasted rainfall of each station, the spatial characteristics of the rainfall process are obtained by spatial interpolation. An actual application to the Tsengwen River basin is conducted to demonstrate the advantage of the proposed model. The results show that the proposed model effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time.
In the second part of the thesis, an effective forecasting model is proposed to yield 1- to 6-h lead time inundation maps for early warning system during typhoons. First, 7-Eleven stores are determined as inundation points for point forecasting. Second, a point forecasting module on the basis of the SVM is developed to yield 1- to 6-h lead time inundation forecasts at each inundation point. Finally, according to the point forecasting results and geographic information, the point forecasts are expanded to the spatial forecasts using the proposed spatial expansion module. An application to Chiayi City, Taiwan, is conducted to demonstrate the superiority of the proposed forecasting model. The results indicate that the proposed model effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time. Moreover, the proposed model is capable of providing accurate inundation maps for 1- to 6-h lead times. In conclusion, the proposed modeling technique is recommended as an alternative to the conventional model to support the disaster warning systems.

並列關鍵字

Support vector machine ； Hourly typhoon rainfall forecasting ； Inundation forecasting ； Inundation map ； Disaster warning system

參考文獻

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