2007-06-012024-05-17https://scholars.lib.ntu.edu.tw/handle/123456789/684729摘要：本研究採用類神經網路來建構流量預報模式。一般的預報模式多以前期雨量及流量作為模式輸入項，對於颱風因子之討論則相當缺乏。吾人收集颱風特徵因子、雨量因子及流量因子作為預報模式可能的輸入項，並經由相關性分析及主成分分析增加對各因子的了解。結果顯示，颱風因子能提供模式有別於雨量及流量因子的訊息。因此本計畫將建立一個以颱風特徵因子、雨量因子及流量因子作為輸入項的颱洪預報模式。另外，預報成果將與不考慮颱風因子之模式進行比較，以了解颱風因子對於提升颱洪預報效果之影響，成果將可作為未來台灣地區颱洪預報之參考。<br> Abstract: The island of Taiwan is situated in one of the main paths of northwestern Pacific typhoons. On average, three to four typhoons attack the island each year. Typhoon rainfall can be a most valuable resource if proper disaster-mitigation measures are made. Otherwise, it can cause serious damage. Improving typhoon-flood forecasting is always an important task of flood management in Taiwan. However, in order to develop a physically based mathematical model for typhoon-flood forecasting, one must know the behaviors of the physical process, which is not an easy task. In this study, an artificial neural network (ANN) is used to construct a typhoon-flood forecasting model. First, correlation analysis and principal components analysis (PCA) are employed to analyze the collected data. Second, the model configuration is evaluated using six typhoon characteristics, which are capable of showing the trend of rainfall when a typhoon is nearby. Finally, the proposed forecasting model is applied to actual typhoon events and the results demonstrate that the proposed model can produce reasonable forecasts.颱風特徵倒傳遞類神經網路洪水預報長延時預報typhoon characteristicsback-propagation neural networkflood forecastinglong-term forecasting