Journal
APPLIED SCIENCES-BASEL
Volume 10, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/app10113984
Keywords
XGBoost; LightGBM; LSTM; bidirectional LSTM; CNNLSTM; GRU; PM2.5; CMAQ
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Funding
- National Strategic Project-Fine particle of the National Research Foundation of Korea (NRF) - Ministry of Science and ICT (MSIT)
- Ministry of Health and Welfare (MOHW) [NRF-2017M3D8A1092022]
- Institute of Information & communications Technology Planning & Evaluation (IITP) - Korean government (MSIT) [2019-0-01842]
- Ministry of Environment (ME)
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Air pollution not only damages the environment but also leads to various illnesses such as respiratory tract and cardiovascular diseases. Nowadays, estimating air pollutants concentration is becoming very important so that people can prepare themselves for the hazardous impact of air pollution beforehand. Various deterministic models have been used to forecast air pollution. In this study, along with various pollutants and meteorological parameters, we also use the concentration of the pollutants predicted by the community multiscale air quality (CMAQ) model which are strongly related to PM2.5 concentration. After combining these parameters, we implement various machine learning models to predict the hourly forecast of PM2.5 concentration in two big cities of South Korea and compare their results. It has been shown that Long Short Term Memory network outperforms other well-known gradient tree boosting models, recurrent, and convolutional neural networks.
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