Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7466926 | Environmental Science & Policy | 2016 | 15 Pages |
Abstract
The analysis of findings points out that the prediction of extreme concentrations is best performed by integrating the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the concentration's dependency upon wind characteristics. So, the Neural Network can be exploited to highlight the involved functional forms and improve the ARIMAX model specification. In the end, the study shows that the ability to forecast exceedances of legal pollution limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a lower threshold than the normative one.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Mario Catalano, Fabio Galatioto, Margaret Bell, Anil Namdeo, Angela S. Bergantino,