Predicting and Analyzing Air Quality Features Effectively using a Hybrid Machine Learning Model

Abstract

The problem of atmospheric air pollution is one of the key environmental problems. In order to determine the factors that make the greatest contribution to air pollution and to counter them in a timely manner, it becomes necessary to constantly monitor the air environment. Currently, monitoring is carried out at stationary sources of pollutants, however, the share of pollution by exhaust gases of motor vehicles has increased. Thus, in order to obtain an objective picture, it is necessary to monitor pollution by motor vehicles, which, with the classical approach, using a variety of gas analyzers, is extremely costly. It is proposed to assess the state of the atmosphere indirectly, through calculations, based on the state of weather conditions, terrain, traffic intensity and car models, from which it is possible to obtain information on the type and amount of emitted pollutants. The article discusses the applicability of machine learning algorithms to the problem of predicting the state of air pollution. A review of the main prediction models was carried out, as well as the effectiveness of their application. Model prediction time estimates are obtained for a fixed error value.