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

Toxics, Journal Year: 2025, Volume and Issue: 13(4), P. 254 - 254

Published: March 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.

Language: Английский

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Seasonality of dust optical depth over Arabian Peninsula using MIDAS data

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 25

Published: April 28, 2025

Language: Английский

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The impact of natural factors and human activities on the dust-vulnerable regions in central Iran

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: May 6, 2025

Language: Английский

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