Forecasting ozone threshold exceedances in urban background areas using supervised classification and easy-access information
Classification models to forecast exceedance of the ozone (O3) threshold established by European legislation are rare in literature, as is the focus on background O3, with higher concentrations at city outskirts. This study evaluated the performance of nine classifiers to forecast this threshold exc...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/138159 |
| Acceso en línea: | https://hdl.handle.net/11441/138159 https://doi.org/10.1016/j.apr.2018.04.002 |
| Access Level: | acceso abierto |
| Palabra clave: | Background ozone Forecasting Classification Imbalanced learning Ensembles Cost of learning |
| Sumario: | Classification models to forecast exceedance of the ozone (O3) threshold established by European legislation are rare in literature, as is the focus on background O3, with higher concentrations at city outskirts. This study evaluated the performance of nine classifiers to forecast this threshold exceedance by background O3. Models used five large hourly background O3 data sets (2006–2015), and included temporal features describing the O3 formation dynamic. Bagging and stacking ensembles of such classifiers and their cost of learning were also evaluated. C5.0 and nnet classifiers achieved the best forecasting performance, even at imbalanced learning. Bagging ensembles outperformed stacking approaches, although with little accuracy improvement as compared to classifiers. The cost of learning evidenced similar performance results from reduced fractions of original data sets. The use of these models to forecast background O3 threshold exceedances are encouraged due to the performances obtained and to their easy reproducibility |
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