Machine-learning regression for forecasting air pollution

Abstract

BACKGROUND AND AIM: Many scientific researches have already confirmed the link between air pollution and many other respiratory and cardiovascular diseases. Air quality forecasting may play an important role in decision making where clean air is still a serious challenge for public health services. METHODS: This paper examines the ability of supervised machine-learning regression techniques, namely, feed-forward neural network, tree-based ensemble, and generalized linear methods, to anticipate and manage changes in atmospheric pollutant concentrations. In order to infer the performance of the developed models, daily observed data of air quality, Climate Indices and meteorological parameter’s collected during the years 2006-2016, have been used. RESULTS:The analysis stage of the modeling provided clear and intuitive results regarding air quality in Casablanca City. The proposed models achieve comparable results with good generalization performances in forecasting pollutant parameters. CONCLUSIONS:Results from this work have important implications for understanding and forecasting of air quality parameters in Morocco and showed as promising techniques to be applied in other countries. KEYWORDS: Air pollution, Ozone, Particulate matter, machine learning