A Hybrid Model for Air Quality Prediction Based on Data Decomposition
Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/5/210 |
| _version_ | 1797533937468178432 |
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| author | Shurui Fan Dongxia Hao Yu Feng Kewen Xia Wenbiao Yang |
| author_facet | Shurui Fan Dongxia Hao Yu Feng Kewen Xia Wenbiao Yang |
| author_sort | Shurui Fan |
| collection | DOAJ |
| description | Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R<sup>2</sup>) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R<sup>2</sup> by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method. |
| first_indexed | 2024-03-10T11:22:45Z |
| format | Article |
| id | doaj.art-3c21bd36e91f4f838394753694f1417c |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T11:22:45Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-3c21bd36e91f4f838394753694f1417c2023-11-21T19:53:54ZengMDPI AGInformation2078-24892021-05-0112521010.3390/info12050210A Hybrid Model for Air Quality Prediction Based on Data DecompositionShurui Fan0Dongxia Hao1Yu Feng2Kewen Xia3Wenbiao Yang4School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaAccurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R<sup>2</sup>) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R<sup>2</sup> by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method.https://www.mdpi.com/2078-2489/12/5/210air qualitywavelet decompositionlong short-term memory (LSTM) neural networkautoregressive moving average (ARMA) model |
| spellingShingle | Shurui Fan Dongxia Hao Yu Feng Kewen Xia Wenbiao Yang A Hybrid Model for Air Quality Prediction Based on Data Decomposition Information air quality wavelet decomposition long short-term memory (LSTM) neural network autoregressive moving average (ARMA) model |
| title | A Hybrid Model for Air Quality Prediction Based on Data Decomposition |
| title_full | A Hybrid Model for Air Quality Prediction Based on Data Decomposition |
| title_fullStr | A Hybrid Model for Air Quality Prediction Based on Data Decomposition |
| title_full_unstemmed | A Hybrid Model for Air Quality Prediction Based on Data Decomposition |
| title_short | A Hybrid Model for Air Quality Prediction Based on Data Decomposition |
| title_sort | hybrid model for air quality prediction based on data decomposition |
| topic | air quality wavelet decomposition long short-term memory (LSTM) neural network autoregressive moving average (ARMA) model |
| url | https://www.mdpi.com/2078-2489/12/5/210 |
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