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Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU

... helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short-term forecasting model based on deep learning is proposed for PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration, and the convolutional-based bidirectional ...

Теги: 1d convolutional neural networks , air pollution forecasting , bidirectional gated recurrent unit , deep learning , air pollution , air pollution control , convolution , deep neural networks , recurrent neural networks , wind , aerodynamic diameters , air pollutant
Раздел: ИСЭМ СО РАН


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