[HTML][HTML] A novel recursive model based on a convolutional long short-term memory neural network for air pollution prediction
Deep learning provides a promising approach for air pollution prediction. The existing deep
learning-based predicted models generally consider either the temporal correlations of air …
learning-based predicted models generally consider either the temporal correlations of air …
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
Air pollution is a serious environmental problem that has drawn worldwide attention.
Predicting air pollution in advance has great significance on people's daily health control …
Predicting air pollution in advance has great significance on people's daily health control …
Spatiotemporal prediction of PM2. 5 concentrations at different time granularities using IDW-BLSTM
As air pollution becomes an increasing concern globally, governments, and research
institutions have attached great importance to air quality prediction to help give early …
institutions have attached great importance to air quality prediction to help give early …
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 …
Long short-term memory-Fully connected (LSTM-FC) neural network for PM2. 5 concentration prediction
People have been suffering from air pollution for a decade in China, especially from PM 2.5
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction
Y Ren, S Wang, B Xia - Atmospheric Pollution Research, 2023 - Elsevier
In this study, we combined the Temporal Convolutional Network (TCN) model with the Long
Short-Term Memory (LSTM) network model and applied it to prediction of atmospheric …
Short-Term Memory (LSTM) network model and applied it to prediction of atmospheric …
Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation
Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …
providing an early warning against harmful air pollutants. However, existing methods of air …
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 …
[HTML][HTML] Prediction of PM2.5 Concentration in Ningxia Hui Autonomous Region Based on PCA-Attention-LSTM
W Ding, Y Zhu - Atmosphere, 2022 - mdpi.com
The problem of air pollution has attracted more and more attention. PM2. 5 is a key factor
affecting air quality. In order to improve the prediction accuracy of PM2. 5 concentration and …
affecting air quality. In order to improve the prediction accuracy of PM2. 5 concentration and …
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 …