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Typhoon is the important calamity of Taiwan. The rainfall of typhoon causes enormous economic losses and casualties, especially for northwestwardly typhoons.
There are many factors to form a typhoon and the rainfall from typhoon is also a symbol of typhoon itself. Therefore, rainfall and other characteristics of typhoon should exist some relationships. The purpose of this study is to investigate the major factors that affects rainfall in the northwestwardly typhoon, and then offers the effective method to predict rainfall. In this research at first the six factors affecting rainfall were chosen, as the input variables. They are the minimum atomospheric pressure, maximum wind velocity near typhoon center, move speed of typhoon center, the radius of typhoon, the shortest distance between typhoon center and Taipei monitoring station, station humidity. These variables were considered to have bell-shaped function distribution to set up fuzzy function. Fuzzification transfers the input data and creats the fuzzy database from the method of the neural nets and its computation program MATLAB/ANFIS. Then the resulting rainfalls were obtained.
The estimated rainfall was compared with the observation rainfall of each station. From these comparisons, the learning effects were analyzed until the minimum errors obtained.
Use the typhoon data from the Central Weather Bureau during the period from years 1950 to 2004, to make a fuzzy calculation and analysis by using rainfall data. For the results in the learning stage, the accuracies of the estimate rainfalls for each station were good, especially for the areas located in the north region of Taiwan and the windward region of the Central Mountain. Although the accuracies of rainfalls for the predicting stage were larger than that of the learning stage, their accuracies were acceptable. The results of this study can be used as a reference for the rainfall prediction for the northwestwardly typhoons.
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