Application of wavelet-packet transform driven deep learning method in PM2. 5 concentration prediction: A case study of Qingdao, China
Air pollution is one of the most serious environmental problems faced by human beings, and
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
Understanding the Disparities of PM2. 5 Air Pollution in Urban Areas via Deep Support Vector Regression
In densely populated urban areas, PM2. 5 has a direct impact on the health and quality of
residents' life. Thus, understanding the disparities of PM2. 5 is crucial for ensuring urban …
residents' life. Thus, understanding the disparities of PM2. 5 is crucial for ensuring urban …
Forecasting PM2. 5 levels in Santiago de Chile using deep learning neural networks
Air pollution has been shown to have a direct effect on human health. In particular, PM 2.5
has been proven to be related to cardiovascular and respiratory problems. Therefore, it is …
has been proven to be related to cardiovascular and respiratory problems. Therefore, it is …
Extraction of multi-scale features enhances the deep learning-based daily PM2. 5 forecasting in cities
Characterising the daily PM2. 5 concentration is crucial for air quality control. To govern the
status of the atmospheric environment, a novel hybrid model for PM2. 5 forecasting was …
status of the atmospheric environment, a novel hybrid model for PM2. 5 forecasting was …
Feature selection using a sinusoidal sequence combined with mutual information
G Yuan, L Lu, X Zhou - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Data classification is the most common task in machine learning, and feature selection is the
key step in the classification task. Common feature selection methods mainly analyze the …
key step in the classification task. Common feature selection methods mainly analyze the …
DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting
Exposure to fine particulate matter can easily lead to health issues. PM2. 5 concentrations
are associated with various spatiotemporal factors, which makes the prediction of PM2. 5 …
are associated with various spatiotemporal factors, which makes the prediction of PM2. 5 …
A balanced social LSTM for PM2. 5 concentration prediction based on local spatiotemporal correlation
L Shi, H Zhang, X Xu, M Han, P Zuo - Chemosphere, 2022 - Elsevier
Reliable prediction for the concentration of PM 2.5 has become a hot topic in pollution
prevention. However, the prediction for PM 2.5 concentration remains a challenge, one of …
prevention. However, the prediction for PM 2.5 concentration remains a challenge, one of …
Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2. 5 concentration forecasting
L Jiang, Z Tao, J Zhu, J Zhang, H Chen - Applied Intelligence, 2023 - Springer
In view of the serious harm to human health caused by atmospheric fine particulate matter
(PM2. 5), accurate prediction of high concentrations of PM2. 5 can help to provide timely …
(PM2. 5), accurate prediction of high concentrations of PM2. 5 can help to provide timely …
[HTML][HTML] Spatiotemporal graph neural networks for predicting mid-to-long-term PM2. 5 concentrations
Predicting the concentration of PM 2.5 particles is of critical importance in public health
management because their small size enables them to penetrate deep into the lungs and …
management because their small size enables them to penetrate deep into the lungs and …
GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution
Q Ni, Y Wang, Y Fang - Applied Intelligence, 2022 - Springer
Many crucial tasks in weather prediction require large-scale and long-term spatio-temporal
predictions. However, these tasks usually face three challenges: high feature redundancy …
predictions. However, these tasks usually face three challenges: high feature redundancy …