Constructing Efficient Regional Hazardous Weather Prediction Models through Big Data Analysis

Lee, Jaedong;Lee, Jee-Hyong

  • 투고 : 2016.02.27
  • 심사 : 2016.03.24
  • 발행 : 2016.03.25


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.


Attribute selection;Big data;Hazardous weather;Regional prediction;Support vector machine


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피인용 문헌

  1. A novel approach for optimizing climate features and network parameters in rainfall forecasting pp.1433-7479, 2017,


연구 과제번호 : Developing On-line Open Platform to Provide Local-business Strategy Analysis and User-targeting Visual Advertisement Materials for Micro-enterprise Managers

연구 과제 주관 기관 : IITP, National Research Foundation of Korea (NRF)