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 …
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) …
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 …
Deep learning methods for atmospheric PM2. 5 prediction: A comparative study of transformer and CNN-LSTM-attention
B Cui, M Liu, S Li, Z Jin, Y Zeng, X Lin - Atmospheric Pollution Research, 2023 - Elsevier
A transformer-based method was firstly developed to predict the hourly PM 2.5 concentration
at 12 monitoring stations in Beijing. Convolutional neural network-long short-term memory …
at 12 monitoring stations in Beijing. Convolutional neural network-long short-term memory …
A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities
In modern society, air pollution is an important topic as this pollution exerts a critically bad
influence on human health and the environment. Among air pollutants, Particulate Matter …
influence on human health and the environment. Among air pollutants, Particulate Matter …
Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China
U Pak, J Ma, U Ryu, K Ryom, U Juhyok, K Pak… - Science of the Total …, 2020 - Elsevier
Air pollution is one of the serious environmental problems that humankind faces and also a
hot topic in Northeastern Asia. Therefore, the accurate prediction of PM2. 5 (particulate …
hot topic in Northeastern Asia. Therefore, the accurate prediction of PM2. 5 (particulate …
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 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 …
Modeling air quality prediction using a deep learning approach: Method optimization and evaluation
W Mao, W Wang, L Jiao, S Zhao, A Liu - Sustainable Cities and Society, 2021 - Elsevier
Air pollution is one of the hot issues that attracted widespread attention from urban and
society management. Air quality prediction is to issue an alarm when severe pollution …
society management. Air quality prediction is to issue an alarm when severe pollution …
Air-pollution prediction in smart city, deep learning approach
Over the past few decades, due to human activities, industrialization, and urbanization, air
pollution has become a life-threatening factor in many countries around the world. Among …
pollution has become a life-threatening factor in many countries around the world. Among …