Bibliographic Details
| Title: |
FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment |
| Authors: |
Yu Cong, Ximeng Zhao, Ke Tang, Ge Wang, Yanfei Hu, Yingkui Jiao |
| Source: |
IEEE Access, Vol 10, Pp 1591-1602 (2022) |
| Publisher Information: |
IEEE, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Pollution emergency decision, toxic gas, air pollution prediction, time series, LSTM, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
Real-time monitoring and accurate prediction of toxic gas concentration in the future are of great significance for emergency capability assessment and rescue work. At present, the method of gas concentration prediction based on artificial intelligence still has problems of low accuracy, slow convergence speed and equal feature importance. This paper proposes a feature-aware LSTM model to predict pollutant gas concentration. First of all, we design a set of multi-component toxic gas monitoring equipment that applies in pollution environment, which can at the same time monitor CO, NO2, NH3, HCN, H2S and SO2, six common pollutants; To accurate estimate the toxic gas concentration, we combine the collected the gas data and the environmental parameters and regard them as the input features, and then we obtain toxic gas data based on the sampling policy and the environmental data as our data-set. Finally, we train a FA-LSTM gas concentration prediction model on these data-set. We test the proposed model and compared with ARIMA, ETS and BP network on the same test set. Experimental results show that the proposed model outperforms traditional concentration prediction model. Also, it is better than other state-of-the-art models in predicting accuracy. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9638644/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3133497 |
| Access URL: |
https://doaj.org/article/d58e54d916c3454cbd5c2a9527acf823 |
| Accession Number: |
edsdoj.58e54d916c3454cbd5c2a9527acf823 |
| Database: |
Directory of Open Access Journals |
