과제정보
이 논문은 2021년도 남서울대학교 학술연구비 지원에 의해 연구되었음.
참고문헌
- Ministry of Environment. 2020. Environmental Statistics Yearbook.
- Kang MS et al. 2016. Analysis of the correlation between PM10 concentration characteristics and emissions in major metropolitan cities on the Korean Peninsula, Korean Society of Environmental Sciences. 25(8):1065-1076.
- Kim YK. 2019. Prediction of Citizens' emotions on home mortgage rates using machine learning algorithms. Journal of cadastre & land InfomatiX. 49(1): 65-84.
- Kim WS. 2014. Seoul Metropolitan Government's ultra-fine dust (PM2.5) management plan, Seoul Institute. 2014-182.
- Sung SY . 2019. The status of temporal and spatial distribution of fine dust concentrations and the potential influencing factors are reviewed. National Research Institute of Human Settlements. WP 19-04.
- Yoo JH. 2011. The redundancy analysis of the air pollution monitoring station in Seoul. Graduate School thesis of Seokyung University.
- Lee GH. 2020. Design and analysis of a model for predicting the risk level of fine dust using a complex neural network structure. Graduate degree thesis of Hanyang University.
- Lee SB.2019. Research trends on the effects of fine dust on the human body. BRIC View 2019-T26.
- Jung JC. 2014. Seoul PM10 Spatial Distribution Analysis and Time Series Changes, Journal of the Korean Geographic Information Society. 17(1):61-69. https://doi.org/10.11108/kagis.2014.17.1.061
- Jung JC. 2017. Spatial information application case for appropriate location assessment of PM10 observation network in Seoul city. Journal of cadastre & land InfomatiX. 47(2): 175-184.
- Jung JC. 2019. Selection of new particulate matter monitoring stations using Kernel analysis - Elementary schools, Seoul, Korea. Journal of cadastre & land InfomatiX. 47(2): 175-184.
- Jo KW. 2019. Evaluation of the suitability of machine learning algorithms for predicting fine dust. Paper of the Korean Society of Information and Communication. 23(1):20-26.
- Choi IJ et al.2016.Evaluation of Air Pollution Measurement Network in the Seoul metropolitan area using multivariate analysis method. Korean Society of Environmental Sciences. 25(5):673-681.
- Rochelle Schneider dos Santos et al. 2020. A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain, medRxiv 2020.07.19.20157396.
- Jan Kleine Deters et al. 2017. Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters, Journal of Electrical and Computer Engineering, Volume 2017, Article ID 5106045.
- Hamed Karimian et al. 2019, Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations. Aerosol and Air Quality Research, 19: 1400-1410. https://doi.org/10.4209/aaqr.2018.12.0450
- Guang Yang et al. 2020, A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea, Atomsphere, 11, 348; doi:10.3390/atmos11040348.
- Lary D.J. et al. 2015, Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies. Environmental Health Insights 2015.9(S1):41-52.