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

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.

Predicting Food Consumption to Reduce the Risk of Food Insecurity in Kazakhstan

“…Forecast modelling has been the subject of research in various fundamental areas over the last decade, from economics (Chuku et al 2019;Mohamed 2022;Rakha et al 2021) to ecology (Guo et al 2022;Munch et al 2017;Sun et al 2022;Kujawska et al 2022) and natural hazards (Jain et al 2018;Oishi et al 2015). Nevertheless, forecasting in the context of food security is a relatively new method.…”