Prediction of air quality index based on the SSA-BiLSTM-LightGBM model
Abstract The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people’s lives, reduce pollution control costs...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-32775-2 |
| _version_ | 1797850070712844288 |
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| author | Xiaowen Zhang Xuchu Jiang Ying Li |
| author_facet | Xiaowen Zhang Xuchu Jiang Ying Li |
| author_sort | Xiaowen Zhang |
| collection | DOAJ |
| description | Abstract The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people’s lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995. |
| first_indexed | 2024-04-09T18:55:31Z |
| format | Article |
| id | doaj.art-f2eff5a14f4a49fd8b1f6faf6f729249 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T18:55:31Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f2eff5a14f4a49fd8b1f6faf6f7292492023-04-09T11:16:02ZengNature PortfolioScientific Reports2045-23222023-04-0113111410.1038/s41598-023-32775-2Prediction of air quality index based on the SSA-BiLSTM-LightGBM modelXiaowen Zhang0Xuchu Jiang1Ying Li2Zhongnan University of Economics and LawZhongnan University of Economics and LawZhongnan University of Economics and LawAbstract The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people’s lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995.https://doi.org/10.1038/s41598-023-32775-2 |
| spellingShingle | Xiaowen Zhang Xuchu Jiang Ying Li Prediction of air quality index based on the SSA-BiLSTM-LightGBM model Scientific Reports |
| title | Prediction of air quality index based on the SSA-BiLSTM-LightGBM model |
| title_full | Prediction of air quality index based on the SSA-BiLSTM-LightGBM model |
| title_fullStr | Prediction of air quality index based on the SSA-BiLSTM-LightGBM model |
| title_full_unstemmed | Prediction of air quality index based on the SSA-BiLSTM-LightGBM model |
| title_short | Prediction of air quality index based on the SSA-BiLSTM-LightGBM model |
| title_sort | prediction of air quality index based on the ssa bilstm lightgbm model |
| url | https://doi.org/10.1038/s41598-023-32775-2 |
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