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Analysis of the Impact of Heatwaves in Gwangju using Logistic Regression and Discriminant Analysis

로지스틱 회귀분석과 판별분석을 활용한 광주광역시의 폭염에 미치는 영향분석

  • Youn Su Kim (Department of Computer Science and Statistic, Chosun University) ;
  • Yeong Seon Kong (Department of Computer Science and Statistic, Chosun University) ;
  • In Hong Chang (Department of Computer Science and Statistic, Chosun University)
  • 김윤수 (조선대학교 컴퓨터통계학과) ;
  • 공영선 (조선대학교 전산통계학과) ;
  • 장인홍 (조선대학교 컴퓨터통계학과)
  • Received : 2024.05.24
  • Accepted : 2024.06.13
  • Published : 2024.06.28

Abstract

Abnormal climate is a phenomenon in which meteorological factors such as temperature and precipitation are significantly higher or lower than normal, and is defined by the World Meteorological Organization as a 30-year period. However, over the past 30 years, abnormal climate phenomena have occurred more frequently around the world than in the past. In Korea, abnormal climate phenomena such as abnormally high temperatures on the Korean Peninsula, drought, heatwave and heavy rain in summer are occurring in March 2023. Among them, heatwaves are expected to increase in frequency compared to other abnormal climates. This suggests that heatwave should be recognised as a disaster rather than just another extreme weather event. According to several previous studies, greenhouse gases and meteorological factors are expected to affect heatwaves, so this paper uses logistic regression and discriminant analysis on meteorological element data and greenhouse gas data in Gwangju from 2008 to 2022. We analyzed the impact of heatwaves. As a result of the analysis, greenhouse gases were selected as effective variables for heatwaves compared to the past, and among them, chlorofluorocarbons were judged to have a stronger effect on heatwaves than other greenhouse gases. Since greenhouse gases have a significant impact on heatwaves, in order to overcome heatwaves and abnormal climates, greenhouse gases must be minimized to overcome heatwaves and abnormal climates.

Keywords

Acknowledgement

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

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