Air quality prediction method based on improved BCCSA and deep LSTM
The existing air quality prediction methods rarely consider seasonal factors, and the prediction effect is not good. Therefore, an air quality prediction method based on improved binary chaotic crow search algorithm(BCCSA) and deep long short term memory neural network(LSTM) is proposed. Firstly, th...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2022-06-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000150247 |
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| Summary: | The existing air quality prediction methods rarely consider seasonal factors, and the prediction effect is not good. Therefore, an air quality prediction method based on improved binary chaotic crow search algorithm(BCCSA) and deep long short term memory neural network(LSTM) is proposed. Firstly, the method of seasonal adjustment is proposed to preprocess the collected original air quality data in order to eliminate the influence of season on prediction. Then, an improved BCCSA is proposed to optimize the air quality data. Finally, the self-attention mechanism is added to the deep LSTM to predict the air quality data. The experimental results show that this method can effectively improve the prediction accuracy of air quality. |
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| ISSN: | 0258-7998 |