Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

Justyna Kujawska, Monika Kulisz, Zulfiya Aubakirova

2022

Full text Open access: Gold Metadata (CSL-JSON)

DOI 10.1088/1742-6596/2412/1/012005

Data confidence 70 medium

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Abstract

Abstract Air quality control and its prediction are particularly important for human health and life. Sulfur dioxide constitutes one of the air pollutants that play an important role in air quality pollution. An artificial neural network model was employed to forecast the levels of sulfur dioxide in the air of Zamość (Poland). The measured data of the meteorological station of Zamość in 2017-2019 were used for the model. Temperature (T), relative humidity (RH), wind speed (WS), wind direction (WD), SO2, PM10, NO2, NOx, CO, O3, C6H6 were used as input parameters for building the neural network model. Regression value (R) and Mean Squared Error (MSE) were used to estimation the model. The results show that neural network is capable of predicting the sulfur dioxide levels in the air.