Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM
D Li, J Liu, Y Zhao - Atmosphere, 2022 - mdpi.com
Air pollution is a growing problem and poses a challenge to people's healthy lives. Accurate
prediction of air pollutant concentrations is considered the key to air pollution warning and …
prediction of air pollutant concentrations is considered the key to air pollution warning and …
Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM
Urban particulate matter forecasting is regarded as an essential issue for early warning and
control management of air pollution, especially fine particulate matter (PM2. 5). However …
control management of air pollution, especially fine particulate matter (PM2. 5). However …
A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5)
T Li, M Hua, XU Wu - Ieee Access, 2020 - ieeexplore.ieee.org
PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …
concentration will aggravate the threat to people's health. Therefore, the prediction of …
Prediction of PM2. 5 concentration in urban agglomeration of China by hybrid network model
S Wu, H Li - Journal of Cleaner Production, 2022 - Elsevier
The urban agglomeration area is a heavy disaster area of PM2. 5 pollution, and the problem
of PM2. 5 pollution seriously affects the natural environment and public health. Accurate …
of PM2. 5 pollution seriously affects the natural environment and public health. Accurate …
Pm2. 5 concentration prediction based on cnn-bilstm and attention mechanism
J Zhang, Y Peng, B Ren, T Li - Algorithms, 2021 - mdpi.com
The concentration of PM2. 5 is an important index to measure the degree of air pollution.
When it exceeds the standard value, it is considered to cause pollution and lower the air …
When it exceeds the standard value, it is considered to cause pollution and lower the air …
A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation
Long-term exposure to air environments full of suspended particles, especially PM2. 5,
would seriously damage people's health and life (ie, respiratory diseases and lung cancers) …
would seriously damage people's health and life (ie, respiratory diseases and lung cancers) …
Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area
Y Su, J Li, L Liu, X Guo, L Huang, M Hu - Atmosphere, 2023 - mdpi.com
Prolonged exposure to high concentrations of suspended particulate matter (SPM),
especially aerodynamic fine particulate matter that is≤ 2.5 μm in diameter (PM2. 5), can …
especially aerodynamic fine particulate matter that is≤ 2.5 μm in diameter (PM2. 5), can …
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration
Urban air pollutant concentration prediction is dealing with a surge of massive
environmental monitoring data and complex changes in air pollutants. This requires effective …
environmental monitoring data and complex changes in air pollutants. This requires effective …
RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model
Predicting the concentration of air pollutants is an effective method for preventing pollution
incidents by providing an early warning of harmful substances in the air. Accurate prediction …
incidents by providing an early warning of harmful substances in the air. Accurate prediction …
PM2. 5 concentrations forecasting in Beijing through deep learning with different inputs, model structures and forecast time
J Yang, R Yan, M Nong, J Liao, F Li, W Sun - Atmospheric Pollution …, 2021 - Elsevier
Timely and accurate air quality forecasting is of great significance for prevention and
mitigation of air pollution. However, most of the previous forecasting models only considered …
mitigation of air pollution. However, most of the previous forecasting models only considered …
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