A Study on Pattern Recognition and Cluster Analysis for Rainfall Forecasting
Date Issued
2006
Date
2006
Author(s)
Lin, Fu-Ru
DOI
zh-TW
Abstract
Recently, the research of artificial intelligence is full of vitality. The Artificial Neural Networks (ANNs) is one of the most representative of achievements. The ANNs can divide into two kinds: supervised learning and unsupervised learning. Unsupervised learning has powerful ability of holding error. A clustered construction, even the input data is imperfect, still can identify the coordinate data from the remainder data. Take the result of recognition into output, it will become the output of forecasting. In connection of the characteristic, the research brings up the pattern recognition and cluster analysis in statistics to classify the rainfall in space and time and to forecast. It is intended to build an all-purpose forecast model. Whenever any data in rain gage is missing, the model also can hold on.
Based on the consideration of factors in meteorology and geography, this research assumes that rainfall exist certain pattern of space and time. A procedure of rainfall forecast of model construction is proposed. Only rainfall data of space and time in the previous time are needed to forecast the condition of rainfall next time steps. It doesn’t need any other condition in meteorology and in climate. Present proposed forecast model is tested using historical rainfall data in Danshui River basin. Reasonably good results have been observed. It indicates that the model will perform better every time when new rainfall patterns are integrated.
Subjects
類神經網路
型態辨識
集群分析
淡水河流域
降雨預報
ANNs
pattern recognition
cluster analysis
Danshui River basin
rainfall forecast
Type
thesis
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