A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City
Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM<sub>2.5</sub> concentrations was integrated with wavelet (W), convolutional neural network (CNN), b...
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MDPI AG
2025-03-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/13/4/254 |
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| author | Zhenfang He Qingchun Guo Zhaosheng Wang Xinzhou Li |
| author_facet | Zhenfang He Qingchun Guo Zhaosheng Wang Xinzhou Li |
| author_sort | Zhenfang He |
| collection | DOAJ |
| description | Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM<sub>2.5</sub> concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU). The data for meteorological factors and air pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs to the models. The W-CNN-BiGRU-BiLSTM hybrid model demonstrated strong performance during the predicting phase, achieving an R (correlation coefficient) of 0.9952, a root mean square error (RMSE) of 1.4935 μg/m<sup>3</sup>, a mean absolute error (MAE) of 1.2091 μg/m<sup>3</sup>, and a mean absolute percentage error (MAPE) of 7.3782%. Correspondingly, the accurate prediction of surface PM<sub>2.5</sub> concentrations is beneficial for air pollution control and urban planning. |
| format | Article |
| id | doaj-art-353f7389b65c4e6aa74f8205c3ba525d |
| institution | DOAJ |
| issn | 2305-6304 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-353f7389b65c4e6aa74f8205c3ba525d2025-08-20T03:13:45ZengMDPI AGToxics2305-63042025-03-0113425410.3390/toxics13040254A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou CityZhenfang He0Qingchun Guo1Zhaosheng Wang2Xinzhou Li3School of Geography and Environment, Liaocheng University, Liaocheng 252000, ChinaSchool of Geography and Environment, Liaocheng University, Liaocheng 252000, ChinaNational Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, ChinaSurface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM<sub>2.5</sub> concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU). The data for meteorological factors and air pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs to the models. The W-CNN-BiGRU-BiLSTM hybrid model demonstrated strong performance during the predicting phase, achieving an R (correlation coefficient) of 0.9952, a root mean square error (RMSE) of 1.4935 μg/m<sup>3</sup>, a mean absolute error (MAE) of 1.2091 μg/m<sup>3</sup>, and a mean absolute percentage error (MAPE) of 7.3782%. Correspondingly, the accurate prediction of surface PM<sub>2.5</sub> concentrations is beneficial for air pollution control and urban planning.https://www.mdpi.com/2305-6304/13/4/254deep learningANNLSTMGRUCNNBiLSTM |
| spellingShingle | Zhenfang He Qingchun Guo Zhaosheng Wang Xinzhou Li A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City Toxics deep learning ANN LSTM GRU CNN BiLSTM |
| title | A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City |
| title_full | A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City |
| title_fullStr | A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City |
| title_full_unstemmed | A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City |
| title_short | A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM<sub>2.5</sub> Concentrations in Guangzhou City |
| title_sort | hybrid wavelet based deep learning model for accurate prediction of daily surface pm sub 2 5 sub concentrations in guangzhou city |
| topic | deep learning ANN LSTM GRU CNN BiLSTM |
| url | https://www.mdpi.com/2305-6304/13/4/254 |
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