Air quality prediction using CT-LSTM

J Wang, J Li, X Wang, J Wang, M Huang - Neural Computing and …, 2021 - Springer
With the development of industry, air pollution has become a serious problem. It is very
important to create an air quality prediction model with high accuracy and good …

Development of a PM2. 5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area, Republic of Korea

H Chang-Hoi, I Park, HR Oh, HJ Gim, SK Hur… - Atmospheric …, 2021 - Elsevier
Abstract The National Institute of Environmental Research, the Ministry of Environment, has
been forecasting the concentrations of particulate matter (PM) with a diameter≤ 2.5 μm (PM …

Soft computing applications in air quality modeling: Past, present, and future

MM Rahman, M Shafiullah, SM Rahman… - Sustainability, 2020 - mdpi.com
Air quality models simulate the atmospheric environment systems and provide increased
domain knowledge and reliable forecasting. They provide early warnings to the population …

Meteorological pattern analysis assisted daily PM2. 5 grades prediction using SVM optimized by PSO algorithm

W Liu, G Guo, F Chen, Y Chen - Atmospheric Pollution Research, 2019 - Elsevier
Daily PM2. 5 level has significant influence on human health, which is attracting increasing
attention. The prediction of PM2. 5 grades has thus become an important factor closely …

Estimation of the visibility in Seoul, South Korea, based on particulate matter and weather data, using machine-learning algorithm

BY Kim, JW Cha, KH Chang, C Lee - Aerosol and Air Quality Research, 2022 - aaqr.org
Visibility is an important indicator of air quality and of any consequent meteorological and
climate change. Therefore, visibility in Seoul, which is the most polluted city in South Korea …

PM2.5 Forecast in Korea using the Long Short-Term Memory (LSTM) Model

CH Ho, I Park, J Kim, JB Lee - Asia-Pacific Journal of Atmospheric …, 2023 - Springer
Abstract The National Institute of Environmental Research, under the Ministry of
Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of …

Untangling the contribution of input parameters to an artificial intelligence PM2. 5 forecast model using the layer-wise relevance propagation method

D Kim, CH Ho, I Park, J Kim, LS Chang… - Atmospheric Environment, 2022 - Elsevier
The recurrent neural network (RNN), an artificial intelligence algorithm, applied to the
predictions based on the Community Multiscale Air Quality operational model has …

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

XN Bui, CW Lee, H Nguyen, HB Bui, NQ Long, QT Le… - Applied Sciences, 2019 - mdpi.com
Featured Application This study provided a new artificial intelligence system (ie, PSO-SVR)
to predict and control PM10 concentration induced by drilling operations in open-pit mines …

Roles of meteorological factors in inter-regional variations of fine and coarse PM concentrations over the Republic of Korea

G Lee, YG Lee, E Jeong, CH Ho - Atmospheric Environment, 2021 - Elsevier
This study examined meteorological effects on airborne concentrations of particulate matter
(PM) of< 2.5 μm (PM 2.5) and 2.5–10 μm (PM 2.5–10) diameter in 16 regions of Korea …

Systematic bias of WRF-CMAQ PM10 simulations for Seoul, Korea

SK Hur, CH Ho, J Kim, HR Oh, YS Koo - Atmospheric Environment, 2021 - Elsevier
For evaluating the performance of the Korean operational air quality model (Weather
Research and Forecasting-Community Multiscale Air Quality), we compared the simulated …