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Open AccessReview

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

1
Intelligent & Interactive Systems Lab (SI2 Lab), Universidad de Las Américas, 170125 Quito, Ecuador
2
Department of Electrical Engineering, CTS/UNINOVA, Nova University of Lisbon, 2829-516 Monte de Caparica, Portugal
3
Grupo de Investigación en Biodiversidad, Medio Ambiente y Salud, Universidad de Las Américas, 170125 Quito, Ecuador
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2570; https://doi.org/10.3390/app8122570
Received: 15 November 2018 / Revised: 6 December 2018 / Accepted: 8 December 2018 / Published: 11 December 2018
(This article belongs to the Special Issue Monitoring and Modeling: Air Quality Evaluation Studies)
Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles). View Full-Text
Keywords: atmospheric pollution; predictive models; data mining; multiple correspondence analysis atmospheric pollution; predictive models; data mining; multiple correspondence analysis
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Rybarczyk, Y.; Zalakeviciute, R. Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review. Appl. Sci. 2018, 8, 2570.

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