NARX-SP NEURAL NETWORK MODELS FOR AIR QUALITY PREDICTION FOR THE 24TH AND 48TH HOUR AHEAD

Abstract

Neural networks are important method of machine learning that can be used to predict air quality with high accuracy. Using NARX-SP neural network type, several neural network models are developed to predict concentration of air pollutants in Sarajevo for two prediction cases, for 24th and 48th hour ahead, with different combinations of inputs and outputs. The data used in this paper contain hourly values of meteorological parameters (air humidity, pressure and temperature, wind speed and direction) and concentrations of SO2, PM10, NO2, O3 and CO from 2016 to 2018. Optimal models are selected for both prediction cases. It is concluded that the optimal models have very good performances and can be used to predict concentration of pollutants in Sarajevo with great accuracy and contribute to improve quality of life. By adequate application of optimal models, concentration of air pollutants can be predicted for each hour over the next 48 hours.

Keywords

• neural networks
• narx-sp
• air quality
• concentration of air pollutants
• prediction