2006-05-042024-05-14https://scholars.lib.ntu.edu.tw/handle/123456789/660078摘要：本計畫建立一個含有兩隱藏層之倒傳遞類神經網路，用以預報颱風來臨時之雨量測站降雨量。類神經網路颱風降雨預報模式，透過類神經網路網路處理非線性關係的能力，將複雜的颱風降雨機制記憶在類神經網路架構內，以達到推估颱風降雨之目的。本計畫採用兩層隱藏層之類神經網路作為颱風降雨之預報模式。其中，本研究應用半變異圖先決定預報模式所需之附近雨量站數目，並經由賀伯及尼爾森兩個颱風之模擬結果發現，此種預報模式對於颱風降雨之預報有更加精確之結果。另外，由結果亦可得知，由於過多之空間資訊反而會增加過多之白噪音，因此降低網路模式之預報效果<br> Abstract: In this project, a neural network with two hidden layers is developed to forecast typhoon rainfall. First, the model configuration is evaluated using eight typhoon characteristics. The forecasts for two typhoons based on only the typhoon characteristics are capable of showing the trend of rainfall when a typhoon is nearby. Furthermore, the influence of spatial rainfall information on rainfall forecasting is considered for improving the model design. A semivariogram is also applied to determine the required number of nearby rain gauges whose rainfall information will be used as input to the model. With the typhoon characteristics and the spatial rainfall information as input to the model, the forecasting model can produce reasonable forecasts. It is also found that too much spatial rainfall information cannot improve the generalization ability of the model, because the inclusion of irrelevant information adds noise to the network and undermines the performance of the network.類神經網路颱風降雨預報模式neural networktyphoon rainfallforecasting model