A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City DOI Creative Commons

Zhenfang He,

Qingchun Guo, Zhaosheng Wang

и другие.

Toxics, Год журнала: 2025, Номер 13(4), С. 254 - 254

Опубликована: Март 28, 2025

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 PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (BiGRU). The data meteorological factors pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs the models. W-CNN-BiGRU-BiLSTM demonstrated strong performance during phase, achieving an R (correlation coefficient) of 0.9952, root mean square error (RMSE) 1.4935 μg/m3, absolute (MAE) 1.2091 percentage (MAPE) 7.3782%. Correspondingly, accurate is beneficial control urban planning.

Язык: Английский

Particulate Matter and Volatile Organic Compounds Emissions and Their Health Impact: Spotlight on Solid Fuel Combustion DOI Creative Commons
Jian Sun, Meng Wang, Jie Tian

и другие.

Toxics, Год журнала: 2025, Номер 13(2), С. 88 - 88

Опубликована: Янв. 24, 2025

Solid fuel combustion, while a crucial source of energy in many regions around the world, remains one primary contributors to air pollution, with significant implications for human health [...]

Язык: Английский

Процитировано

0
A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City DOI Creative Commons

Zhenfang He,

Qingchun Guo, Zhaosheng Wang

и другие.

Toxics, Год журнала: 2025, Номер 13(4), С. 254 - 254

Опубликована: Март 28, 2025

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 PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (BiGRU). The data meteorological factors pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs the models. W-CNN-BiGRU-BiLSTM demonstrated strong performance during phase, achieving an R (correlation coefficient) of 0.9952, root mean square error (RMSE) 1.4935 μg/m3, absolute (MAE) 1.2091 percentage (MAPE) 7.3782%. Correspondingly, accurate is beneficial control urban planning.

Язык: Английский

Процитировано

0