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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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2022-06-01
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| Series: | Dianzi Jishu Yingyong |
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| Online Access: | http://www.chinaaet.com/article/3000150247 |
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| author | Wei Shiyue Xu Hongzhen |
| author_facet | Wei Shiyue Xu Hongzhen |
| author_sort | Wei Shiyue |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c43a430b174d4c90af8d5f019b6025f0 |
| institution | DOAJ |
| issn | 0258-7998 |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj-art-c43a430b174d4c90af8d5f019b6025f02025-08-20T02:44:22ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982022-06-01486283210.16157/j.issn.0258-7998.2227313000150247Air quality prediction method based on improved BCCSA and deep LSTMWei Shiyue0Xu Hongzhen1School of Information Engineering，East China University of Technology，Nanchang 330013，ChinaSchool of Information Engineering，East China University of Technology，Nanchang 330013，ChinaThe 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.http://www.chinaaet.com/article/3000150247air qualityseasonal adjustmentimproved binary chaotic crow search algorithm(bccsa)deep long short term memory(lstm)self-attention mechanism |
| spellingShingle | Wei Shiyue Xu Hongzhen Air quality prediction method based on improved BCCSA and deep LSTM Dianzi Jishu Yingyong air quality seasonal adjustment improved binary chaotic crow search algorithm(bccsa) deep long short term memory(lstm) self-attention mechanism |
| title | Air quality prediction method based on improved BCCSA and deep LSTM |
| title_full | Air quality prediction method based on improved BCCSA and deep LSTM |
| title_fullStr | Air quality prediction method based on improved BCCSA and deep LSTM |
| title_full_unstemmed | Air quality prediction method based on improved BCCSA and deep LSTM |
| title_short | Air quality prediction method based on improved BCCSA and deep LSTM |
| title_sort | air quality prediction method based on improved bccsa and deep lstm |
| topic | air quality seasonal adjustment improved binary chaotic crow search algorithm(bccsa) deep long short term memory(lstm) self-attention mechanism |
| url | http://www.chinaaet.com/article/3000150247 |
| work_keys_str_mv | AT weishiyue airqualitypredictionmethodbasedonimprovedbccsaanddeeplstm AT xuhongzhen airqualitypredictionmethodbasedonimprovedbccsaanddeeplstm |