.png){kind=icon}
.png){kind=icon}
Return
Decomposition and Forecasting of Concentrations of Highly Volatile Atmospheric Pollutants in Plateau Cities Based on the VMD-CNN-BiLSTM Framework
.png){kind=icon}
OA
AI
J
F
X
W
Z
X
H
DOI：10.1016/j.eti.2025.104717.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• A new combined prediction model is proposed to improve the accuracy of urban pollutant prediction in the plateau. • The difference of different combination models in the prediction of urban pollutants in plateau was studied. • The accuracy of different combination models in predicting pollutants was evaluated. • It provides a new idea for the prediction of urban pollutants in the plateau. • From the perspectives of signal characteristics and pollution source analysis, this study reveals why the same advanced forecasting framework exhibits significant performance differences when handling PM2.5 and PM10 with distinct physical properties.
Keywords:
Air quality prediction
Plateau cities
Bidirectional Long Short-Term Memory Network
Variational Modal Decomposition
Convolutional Neural Network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
4.0K
Citations:
1.9W
Organization
Cited Papers