A hybrid CNN-LSTM model for predicting PM2. 5 in Beijing based on spatiotemporal correlation
D Chen, G Wang, Z Xinyue, Q Liu… - … and Ecological Statistics, 2021 - search.proquest.com
Long-term exposure to air environments full of suspended particles, especially PM 2.5,
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) …
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) …
Impacts of Temporal Resolution and Spatial Information on Neural-Network-Based PM2.5 Prediction Model
Z Silin, R Xiaochen, W Chenggong… - Beijing Da Xue Xue …, 2020 - search.proquest.com
Taking Beijing as an example and using the data of air quality monitoring stations from 2015
to 2018, the impacts of temporal resolution and spatial information on the PM2. 5 …
to 2018, the impacts of temporal resolution and spatial information on the PM2. 5 …
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 …
[HTML][HTML] Prediction of PM2. 5 concentration based on a CNN-LSTM neural network algorithm
X Bai, N Zhang, X Cao, W Chen - PeerJ, 2024 - peerj.com
Abstract Fine particulate matter (PM 2.5) is a major air pollutant affecting human survival,
development and health. By predicting the spatial distribution concentration of PM 2.5 …
development and health. By predicting the spatial distribution concentration of PM 2.5 …
[HTML][HTML] 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 …
[HTML][HTML] 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 …
[HTML][HTML] Research on PM2. 5 concentration prediction based on the CE-AGA-LSTM model
X Wu, C Zhang, J Zhu, X Zhang - Applied Sciences, 2022 - mdpi.com
The PM2. 5 index is an important basis for measuring the degree of air pollution. The
accurate prediction of PM2. 5 concentration has an important guiding role in air pollution …
accurate prediction of PM2. 5 concentration has an important guiding role in air pollution …
Machine-learning-based model and simulation analysis of PM2. 5 concentration prediction in Beijing
In recent years, the air quality in China has become a matter of serious concern. Among the
available indicators for evaluating air quality, PM2. 5 is one of the most important. It …
available indicators for evaluating air quality, PM2. 5 is one of the most important. It …
A hybrid CLSTM-GPR model for forecasting particulate matter (PM2. 5)
J He, X Li, Z Chen, W Mai, C Zhang, X Wan… - Atmospheric Pollution …, 2023 - Elsevier
PM 2.5 concentration is closely related to air pollution and human health, which should be
predicted accurately and reliably. In this study, we proposed a hybrid model combining …
predicted accurately and reliably. In this study, we proposed a hybrid model combining …