Name
Title | ||
|---|---|---|
Constructing a PM concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. |
Abstract | ||
|---|---|---|
Air pollution problems have a severe effect on the natural environment and public health. The application of machine learning to air pollutant data can result in a better understanding of environmental quality. Of these methods, the deep learning method has proven to be a very efficient and accurate method to forecast complex air quality data. This paper proposes a deep learning model based on an auto-encoder and bidirectional long short-term memory (Bi-LSTM) to forecast PM2.5 concentrations to reveal the correlation between PM2.5 and multiple climate variables. The model comprises several aspects, including data preprocessing, auto-encoder layer, and Bi-LSTM layer. The performance of the proposed model was verified based on a real-world air pollution dataset, and the results indicated this model can improve the prediction accuracy in an experimental scenario. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.envsoft.2019.104600 | Environmental Modelling & Software |
Keywords | DocType | Volume |
Deep learning,Auto-encoder,Bi-LSTM,Data preprocessing,PM2.5 concentration prediction,Air pollution | Journal | 124 |
ISSN | Citations | PageRank |
1364-8152 | 2 | 0.41 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| bo zhang | 1 | 20 | 5.75 |
| Hanwen Zhang | 2 | 2 | 0.41 |
| Gengming Zhao | 3 | 2 | 0.41 |
| Jie Lian | 4 | 2 | 0.41 |