온열질환자 예측을 위한 최적의 지표 분석
Analysis of Optimal Index for Heat Morbidity
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초록
본 연구의 목적은 온열질환자를 설명, 예측하기 위한 최적의 폭염 관련 지표를 선정하고 예측하여 실효성을 확인하는 것이다. 2021년부터 2023년까지의 온열질환 응급실감시체계 데이터와 기상청 AWS 데이터를 기반으로 일 평균 기온, 일 최고 기온, 일 평균WBGT, 일 최고WBGT 값을 계산하여 회귀분석을 진행하였다. 분석 결과 네 가지 지표 중 일 최고 WBGT가 R2 값 0.81, RMSE 0.98로 가장 적합한 지표로 나타났으며 그 임계값은 29.94도로 나타났다. 전체 분석 기간 중 해당 임계값을 초과하는 날은 총 91일이었으며 이 때 발생한 환자수는 339명으로 나타났다. 일 최고WBGT의 회귀식을 통해 2021년부터 2023년까지의 온열질환자 수를 예측한 결과 매년 10명 미만의 오차를 보여 정확성이 상당히 높은 것을 확인할 수 있었다. 지속적인 연구를 통해 데이터 및 분석 방법을 고도화한다면, 폭염 피해를 예측 및 저감하는데 도움이 될 수 있을 것이다.
Abstract
The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration’s Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature(WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R2 value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.
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