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Constructing Efficient Regional Hazardous Weather Prediction Models through Big Data Analysis
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 Title & Authors
Constructing Efficient Regional Hazardous Weather Prediction Models through Big Data Analysis
Lee, Jaedong; Lee, Jee-Hyong;
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 Abstract
In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.
 Keywords
Attribute selection;Big data;Hazardous weather;Regional prediction;Support vector machine;
 Language
English
 Cited by
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