Prediction of Temperature and Heat Wave Occurrence for Summer Season Using Machine Learning

기계학습을 활용한 하절기 기온 및 폭염발생여부 예측

  • Kim, Young In (Dept. of Civil Engineering, Hongik Univ.) ;
  • Kim, DongHyun (Dept. of Civil Engineering, Hongik Univ.) ;
  • Lee, Seung Oh (Dept. of Civil Engineering, Hongik Univ.)
  • 김영인 (홍익대학교 토목공학과) ;
  • 김동현 (홍익대학교 토목공학과) ;
  • 이승오 (홍익대학교 토목공학과)
  • Received : 2020.04.15
  • Accepted : 2020.06.29
  • Published : 2020.06.30


Climate variations have become worse and diversified recently, which caused catastrophic disasters for our communities and ecosystem including economic property damages in Korea. Heat wave of summer season is one of causes for such damages of which outbreak tends to increase recently. Related short-term forecasting information has been provided by the Korea Meteorological Administration based on results from numerical forecasting model. As the study area, the ◯◯ province was selected because of the highest mortality rate in Korea for the past 15 years (1998~2012). When comparing the forecasted temperatures with field measurements, it showed RMSE of 1.57℃ and RMSE of 1.96℃ was calculated when only comparing the data corresponding to the observed value of 33℃ or higher. The forecasting process would take at least about 3~4 hours to provide the 4 hours advanced forecasting information. Therefore, this study proposes a methodology for temperature prediction using LSTM considering the short prediction time and the adequate accuracy. As a result of 4 hour temperature prediction using this approach, RMSE of 1.71℃ was occurred. When comparing only the observed value of 33℃ or higher, RMSE of 1.39℃ was obtained. Even the numerical prediction model of the whole range of errors is relatively smaller, but the accuracy of prediction of the machine learning model is higher for above 33℃. In addition, it took an average of 9 minutes and 26 seconds to provide temperature information using this approach. It would be necessary to study for wider spatial range or different province with proper data set in near future.


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