Predictive analytics of PM10 concentration levels using detailed traffic data

PM10 particles impose significant risks to human health and the well-being of individuals in general. However, due to the complexity of the inner-correlations between influencing environmental factors, the holistic approach to predictive analytics of PM10 concentration levels is a challenging task yet to be undertaken. We base this study on the rationale that a prediction model is suitable for making accurate estimations involving knowledge about the hidden interactions that govern them. In addition to the model's precision, it is, therefore, beneficial to provide a model that is interpretable, as this can assist in the decision about how and which prevention actions to take. For this purpose, a Genetic Algorithm is proposed that carries out multiple regression analysis by searching for the optimal fictional definition of a prediction model. As such, the obtained model is human interpretable, where the preliminary analysis conducted within this study proved its compliance with the existing studies, while the model itself proved to be considerably more accurate than the present state-of-the-art.