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2024 Vol.11, Issue 4 Preview Page

Original Article

Effects of Demographic and Meteorological Factors on Incidence of Heat-related Illness using Machine Learning

머신러닝을 이용한 인구사회학적 요인과 기상요인이 온열질환자 발생에 미치는 영향 분석

Ah Reum Han1*
,
Taehoon Lee2
,
Sun Jun Lee3
,
Hansol Kwak4
한 아름1*
,
이 태훈2
,
이 선준3
,
곽 한솔4
1Associate Researcher, Ecological Observation Team, National Institute of Ecology, Seocheon 33657, Korea
2Senior Researcher, Future Technology Research Institute, SR Inc., Seoul 06367, Korea
3Deputy Director, Data and Information Management Division, Ministry of Personnel Management, Sejong 30128, Korea
4Assistant Deputy Director, Resident Service Division, Ministry of the Interior and Safety, Sejong 30112, Korea
1국립생태원 기후생태관측팀 전임연구원
2에스알 미래연구원 선임연구원
3인사혁신처 데이터정보담당관실 전산사무관
4행정안전부 주민과 주무관
*Corresponding Author
31 December 2024. pp. 186-202
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Abstract

Increase of extreme temperatures, including heatwaves and tropical nights, is causing significant human and property damage worldwide. In South Korea, the number of heat-related illnesses has exceeded fatalities from heavy rain and typhoons, showing a growing trend. Thus, proactive measures are urgently needed. This study aimed to predict the number of heat-related illness occurrence using sociodemographic and meteorological factors from 229 districts in South Korea. Data from the Korea Disease Control and Prevention Agency (2021–2023), including heat-related illness cases, 24 of 35 sociodemographic variables and 32 of 70 meteorological variables, were anlyzed for model development. A prediction model was built using machine learning models (Ridge, Support Vector Regressor, Decision Tree Regressor, Gradient Boosting, Random Forest Regressor, XGBoost, LightGBM) to forecast the number of heat-related illnesses per 100,000 people. The XGBoost model (R² = 0.76) showed the highest explanatory power. Sociodemographic factors such as non-urban population (%), apartment housing area (%), nursing medical institutions status, and population over 65 years old (%) were the most important variables contributing to the increased occurrence of heat-related illnesses. Among meteorological variables, variables indicating sustained high temperatures, such as consecutive days with minimum temperature ≥ 25°C and cumulative days with maximum temperature ≥ 29°C, showed higher importance. These results emphasize that the number of heat-related illnesses is influenced not only by social systems and structures, but also by combined effects of vulnerable populations and meteorological factors. Thus, comprehensive strategies addressing both sociodemographic and meteorological factors are needed for effective prevention.

Keywords
Cumulative heat
Heat-related illnesses
Predictive model
Proportion of communal housing facilities
Proportion of rural population
XGBoost

기온상승과 폭염, 열대야 등 극한기온 일수의 증가로 전 세계적으로 인명과 재산 피해가 급증하고 있고, 국내에서도 온열질환자수는 폭우, 태풍으로 인한 인명피해보다 월등히 높으며 지속적으로 증가하는 경향을 보이고 있어 적극적인 대책 마련이 시급하다. 본 연구에서는 온열질환자 발생을 229개 시군구의 인구사회학적 특성 인자와 기상인자를 사용하여 예측하고자 한다. 질병관리청에서 제공하는 229개 시군구 단위 온열질환자 발생자정보(2021 ~ 2023)와 파생변수를 포함한 인구사회학적 변수 35개 중 24개, 기상변수 70개 중 32개의 변수를 선정하여 온열질환자수 예측 모델을 구축하였다. 전국 229개 지자체를 대상으로 인구 10만명당 온열질환자수를 예측하는데 적용한 기계학습 모델(Ridge, Support Vector Regressor, DecisionTreeRegressor, Gradient Boosting, RandomForestRegressor, XGBoost, LightGBM)을 각각 사용하여 개발되었고, 가장 설명력이 높은 모델은 XGBoost (R2 = 0.76)이었다. 온열질환자 발생수와 관련이 높은 인구사회학적 중요 변수는 비도시인구(%), 공동주거시설면적(%), 요양의료기관현황, 65세 이상 인구수(%)와 같은 인구사회학적 인자였다. 기상변수 중에서는 일최저기온 25°C 이상 연속일수, 일최고기온 29°C 이상 누적일수와 같은 고온의 지속성에 대한 중요도가 높았다. 온열질환자수 예측 모델의 주요 변수 특성을 고려할 때, 열 스트레스로 인한 인명피해는 사회시스템과 구조의 영향이 크지만, 취약 인구와 기상 인자간 복합적인 영향으로 발생하는 것으로 보인다. 따라서 온열질환자 발생 예방 관리를 위해서 취약 집단, 지역 등과 같은 단일 인자들을 대상으로 접근하기보다 인구사회학적 인자, 기상 인자 모두를 고려한 종합적인 취약집단과 지역을 도출한 세심한 관리대책 마련이 필요하다. 따라서 본 연구는 온열질환자 발생 예방 관리를 위해서 인구사회학적 인자와 기상인자를 고려해 취약집단과 지역에 대한 세심한 관리대책을 수립하는데 활용할 수 있다.

키워드
고온지속성
온열질환
예측모델
공동주거시설면적
비도시인구비율
XGBoost
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Information
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 11
  • No :4
  • Pages :186-202
  • Received Date : 2024-12-03
  • Revised Date : 2024-12-20
  • Accepted Date : 2024-12-20
  • DOI :https://doi.org/10.17820/eri.2024.11.4.186
Journal Informaiton Ecology and Resilient Infrastructure Ecology and Resilient Infrastructure
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