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Neural network models for prediction of air pollution index in an industrial city

Panchenko Alina Alikovna  (Lecturer, Sterlitamak branch of Ufa State Petroleum Technological University, Russian Federation)

Rahman Pavel Azizurovich  (Candidate of technical sciences, associate professor, Sterlitamak branch of Ufa State Petroleum Technological University, Russian Federation)

Safarov Ayrat Muratovich  (Doctor of technical sciences, associate professor, Ufa State Petroleum Technological University, Ufa, Russian Federation)

This scientific paper deals with use of the artificial neural networks for the ecological prediction of state of the atmospheric air of an industrial city. The authors offer two types of predictive models for determining of the level of air pollution on the basis of neural networks: a temporal (short-term forecast of the pollutants content in the air) and a spatial (forecast of atmospheric pollution index in any part of the city). The structure and parameters of the offered neural networks, and selection of the learning algorithms, which provides best adequacy of the models based on the neural networks are also observed.

Keywords:air basin, short-term air pollution forecast, neuron, neural network model, feed-forward neural network, Elman neural network, learning algorithm for neural network.
 

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Citation link:
Panchenko A. A., Rahman P. A., Safarov A. M. Neural network models for prediction of air pollution index in an industrial city // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2018. -№05. -С. 121-126
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