Air Quality Forecasting Using Deep Learning Framework

Abstract

Air pollution represents an issue that raises many concerns nowadays, as it has various negative effects on the environment and the economy worldwide. Because of the rapid urbanization, cities are suffering from polluted air, so it is important to predict future air quality. For this purpose, new applications of artificial intelligence should be employed. In this paper, we will present several Machine Learning algorithms, the possible software that can be used for them and the applications used in the field of air quality. Based on the research in the field, we propose CNN and LSTM, Machine Learning models, which can be used to predict air pollution. These algorithms have been tested using time-series for PM₁₀ and PM_2.5 particles. The results showed that and algorithms are the most suitable in forecasting air pollutant concentrations.