Prediction of atmospheric air pollution by PM2.5 particles based on artificial neural networks

Irina V. Del; Alexander V. Starchenko

doi:10.1117/12.2690388

17 October 2023 Prediction of atmospheric air pollution by PM_2.5 particles based on artificial neural networks

Irina V. Del, Alexander V. Starchenko

Proceedings Volume 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 127805I (2023) https://doi.org/10.1117/12.2690388
Event: XXIX International Symposium "Atmospheric and Ocean Optics, Atmospheric Physics", 2023, Moscow, Russian Federation

Abstract

A recurrent neural network model of long short-term memory (LSTM) type implemented to predict atmospheric air pollution by PM_2.5 particles. Based on the distribution of meteorological values and concentrations of main air pollutants known from observations over the previous 6 hours, the task was set to predict PM_2.5 concentrations for the next 3 hours. The total value of the average absolute error over the whole forecast was 2.35 μg/m³.

Citation Download Citation

Irina V. Del and Alexander V. Starchenko "Prediction of atmospheric air pollution by PM_2.5 particles based on artificial neural networks", Proc. SPIE 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 127805I (17 October 2023); https://doi.org/10.1117/12.2690388

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