A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection

Air pollution is considered one of the biggest threats for the ecological system and human existence. Therefore, air quality monitoring has become a necessity in urban and industrial areas. Recently, the emergence of Machine Learning techniques justifies the application of statistical approaches for environmental modeling, especially in air quality forecasting. In this context, we propose a novel feature ranking method, termed as Ensemble of Regressor Chains-guided Feature Ranking (ERCFR) to forecast multiple air pollutants simultaneously over two cities. This approach is based on a combination of one of the most powerful ensemble methods for Multi-Target Regression problems (Ensemble of Regressor Chains) and the Random Forest permutation importance measure. Thus, feature selection allowed the model to obtain the best results with a restricted subset of features. The experimental results reveal the superiority of the proposed approach compared to other state-of-the-art methods, although some cautions have to be considered to improve the runtime performance and to decrease its sensitivity over extreme and outlier values. [Display omitted] •Forecasting multiple air pollutant concentrations simultaneously.•The combination of Multi-Target Regression method and the Random Forest paradigm.•The proposed method ensures better performance in air quality forecast.