Loading...
Please wait, while we are loading the content...
Similar Documents
Haze Prediction Model Using Deep Recurrent Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Shang, Kailin Chen, Ziyi Liu, Zhixin Song, Lihong Zheng, Wenfeng Yang, Bo Liu, Shan Yin, Lirong |
| Copyright Year | 2021 |
| Description | In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes. |
| Starting Page | 1625 |
| e-ISSN | 20734433 |
| DOI | 10.3390/atmos12121625 |
| Journal | Atmosphere |
| Issue Number | 12 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-12-06 |
| Access Restriction | Open |
| Subject Keyword | Atmosphere Industrial Engineering Haze Prediction Pm2.5/pm10 Deep Recurrent Neural Network |
| Content Type | Text |
| Resource Type | Article |
