FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment
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 conver...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9638644/ |
| _version_ | 1818943563637981184 |
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| author | Yu Cong Ximeng Zhao Ke Tang Ge Wang Yanfei Hu Yingkui Jiao |
| author_facet | Yu Cong Ximeng Zhao Ke Tang Ge Wang Yanfei Hu Yingkui Jiao |
| author_sort | Yu Cong |
| collection | DOAJ |
| description | 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, NO<sub>2</sub>, NH<sub>3</sub>, HCN, H<sub>2</sub>S and SO<sub>2</sub>, 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. |
| first_indexed | 2024-12-20T07:29:19Z |
| format | Article |
| id | doaj.art-d58e54d916c3454cbd5c2a9527acf823 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T07:29:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d58e54d916c3454cbd5c2a9527acf8232022-12-21T19:48:28ZengIEEEIEEE Access2169-35362022-01-01101591160210.1109/ACCESS.2021.31334979638644FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant EnvironmentYu Cong0https://orcid.org/0000-0002-8120-4384Ximeng Zhao1https://orcid.org/0000-0003-0782-1509Ke Tang2Ge Wang3Yanfei Hu4Yingkui Jiao5Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaChina Institute for WTO Studies, University of International Business and Economics, Beijing, ChinaChina Institute for WTO Studies, University of International Business and Economics, Beijing, ChinaCollege of Management and Economics, Tianjin University, Tianjin, ChinaSchool of Cyber Security, University of Chinese Academy of Science, Beijing, ChinaState Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, ChinaReal-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, NO<sub>2</sub>, NH<sub>3</sub>, HCN, H<sub>2</sub>S and SO<sub>2</sub>, 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.https://ieeexplore.ieee.org/document/9638644/Pollution emergency decisiontoxic gasair pollution predictiontime seriesLSTM |
| spellingShingle | Yu Cong Ximeng Zhao Ke Tang Ge Wang Yanfei Hu Yingkui Jiao FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment IEEE Access Pollution emergency decision toxic gas air pollution prediction time series LSTM |
| title | FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment |
| title_full | FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment |
| title_fullStr | FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment |
| title_full_unstemmed | FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment |
| title_short | FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment |
| title_sort | fa lstm a novel toxic gas concentration prediction model in pollutant environment |
| topic | Pollution emergency decision toxic gas air pollution prediction time series LSTM |
| url | https://ieeexplore.ieee.org/document/9638644/ |
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