다중선형회귀와 인공신경망을 이용한 2015년 이전 PM₂.₅ 일일 평균 수치 추정 방법론 제안

• 영문명: Daily PM₂.₅ Estimation using Multiple Linear Regression and Artificial Neural Networks Before 2015
• 발행기관: 강원대학교 산업기술연구소
• 저자명: 허진우(Jin-Woo Huh) 박세준(SeJoon Park)
• 간행물 정보: 『산업기술연구』vol.44, 1~7쪽, 전체 7쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

Since 2015, the PM₂.₅ measurement data has been publicly available nationwide in South Korea, but its use is restricted to after 2015, unlike other air pollutants. To overcome this limitation, multiple linear regression and artificial neural network models were developed to predict the daily average PM₂.₅ values in South Korea before 2015. The daily data of air pollution measurement(SO₂, CO, O₃, NO₂, PM₁₀) and meteorological observation data (temperature, humidity, wind speed, atmospheric pressure, precipitation, snowfall) were used as input variables to develop regional prediction models for five regions(Seoul, Incheon, Gwangju, Daejeon, Ulsan) and a national prediction model. The models were developed and validated using the air pollution measurement data after 2015, and applied to predict PM₂.₅ values before 2015. The multiple linear regression model showed R₂ values of 0.80 nationwide, 0.73 in Seoul, and 0.67 in Incheon, which enabled estimation of daily average PM₂.₅ values before 2015. The artificial neural network model showed good prediction power with R₂ values of 0.79 in Gwangju, 0.81 in Daejeon, and 0.72 in Ulsan. The regional prediction models showed good prediction power in most regions, and both the multiple linear regression and artificial neural network models showed good prediction power.

목차

1. 서 론

2. 연구 방법

3. 결 과

4. 결 론

5. 감사의 글

References

키워드

Air pollutant Fine particulate matter Artificial neural networks Multiple linear regression Prediction

참고문헌

• Journal of the Korean Medical Association
• Environmental Pollution
• Journal of Korea Society for Atmospheric Enviroment
• Journal of Korea Society for Atmospheric Enviroment
• Journal of Korea Society for Atmospheric Enviroment
• Atmospheric Environment
• Sicience of The Total Environment
• The R Journal
• John Wiley & Sons
• Applied Sciences