Application of Neural Networks to The Prediction of Gas Pollution of Air

• Autorzy: Małgorzata Pawul
• Języki publikacji: EN

Abstrakty

EN

The issue of projecting the air pollution levels is quite essential from the viewpoint of the necessity to adopt specific prevention measures intended to reduce the pollution concentration in the air. One can apply certain machine learning methods, including neural networks, to build pollution concentration models. Neural networks are characterised by the fact that they can be used to solve the relevant problem when we face shortage of data, or we do not know the analytical relationship between input and output data. Consequently, neural networks can be applied in a number of problems. This paper discusses a possibility to apply neural networks to the prediction of selected gas concentrations in the air, based on the data originating from the measurement networks of the Polish State Environmental Monitoring System, combined with local meteorological data. Forecast results have been presented here for SO2, NO, NO2, and O3 in various locations. The author also discusses the accuracy of the respective forecasts and indicates the relevant contributing factors. (original abstract)

Słowa kluczowe

EN

Air quality; Air pollution; Gas emissions

PL

Jakość powietrza; Zanieczyszczenie powietrza; Emisja gazów

• Czasopismo: New Trends in Production Engineering
• Rocznik: 2019
• Numer: vol. 2(1) cz.II Part II: Selected Organizational Problems in the Mining Industry
• Strony: 515--523

Twórcy

autor

Małgorzata Pawul

• AGH Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

Bibliografia

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Identyfikatory

DOI

10.2478/ntpe-2019-0055