Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Classification Abnormal temperatures based on Meteorological Environment using Random forests

랜덤포레스트를 이용한 기상 환경에 따른 이상기온 분류

  • Youn Su Kim (Department of Computer Science and Statistic, Chosun University) ;
  • Kwang Yoon Song (Department of Computer Science and Statistic, Chosun University) ;
  • In Hong Chang (Department of Computer Science and Statistic, Chosun University)
  • 김윤수 (조선대학교 컴퓨터통계학과) ;
  • 송광윤 (조선대학교 컴퓨터통계학과) ;
  • 장인홍 (조선대학교 컴퓨터통계학과)
  • Received : 2024.02.08
  • Accepted : 2024.02.28
  • Published : 2024.03.30
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Abstract

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

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Acknowledgement

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A6A3A01059888).

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