Multi-objective evolutionary spatio-temporal forecasting of air pollution

• Published in: Future generation computer systems Vol. 136; pp. 15 - 33
• Main Authors: Espinosa, Raquel, Jiménez, Fernando, Palma, José
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-11-2022
• Subjects: Air pollution; Ensemble learning; Evolutionary algorithms; Machine learning; Multi-objective optimization; Spatio-temporal forecasting; Air pollution; Multi-objective optimization; Evolutionary algorithms; Spatio-temporal forecasting; Ensemble learning; Machine learning
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2022.05.020

Nowadays, air pollution forecasting modeling is vital to achieve an increase in air quality, allowing an improvement of ecosystems and human health. It is important to consider the spatial characteristics of the data, as they allow us to infer predictions in those areas for which no information is available. In the current literature, there are a large number of proposals for spatio-temporal air pollution forecasting. In this paper we propose a novel spatio-temporal approach based on multi-objective evolutionary algorithms for the identification of multiple non-dominated linear regression models and their combination in an ensemble learning model for air pollution forecasting. The ability of multi-objective evolutionary algorithms to find a Pareto front of solutions is used to build multiple forecast models geographically distributed in the area of interest. The proposed method has been applied for one-week NO2 prediction in southeastern Spain and has obtained promising results in statistical comparison with other approaches such as the union of datasets or the interpolation of the predictions for each monitoring station. The validity of the proposed spatio-temporal approach is thus demonstrated, opening up a new field in air pollution engineering.
•Modeling is very useful for air quality forecasting with spatio-temporal data.•Evolutionary computation identifies non-dominated models in a distributed area.•Ensemble learning models are effective for the integration of prediction models.