Cargando…
Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels
High concentration episodes for NO2 are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabili...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173015/ https://www.ncbi.nlm.nih.gov/pubmed/34078925 http://dx.doi.org/10.1038/s41598-021-90063-3 |
_version_ | 1783702633883631616 |
---|---|
author | Vasseur, Sebastien Pérez Aznarte, José L. |
author_facet | Vasseur, Sebastien Pérez Aznarte, José L. |
author_sort | Vasseur, Sebastien Pérez |
collection | PubMed |
description | High concentration episodes for NO2 are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting, as oposed to point-forecasting, is a family of techniques that allow for the prediction of the expected distribution function instead of a single future value. In the case of NO(2), it allows for the calculation of future chances of exceeding thresholds and to detect pollution peaks. However, there is a lack of comparative studies for probabilistic models in the field of air pollution. In this work, we thoroughly compared 10 state of the art quantile regression models, using them to predict the distribution of NO(2) concentrations in a urban location for a set of forecasting horizons (up to 60 hours into the future). Instead of using directly the quantiles, we derived from them the parameters of a predicted distribution, rendering this method semi-parametric. Amongst the models tested, quantile gradient boosted trees show the best performance, yielding the best results for both expected point value and full distribution. However, we found the simpler quantile k-nearest neighbors combined with a linear regression provided similar results with much lower training time and complexity. |
format | Online Article Text |
id | pubmed-8173015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81730152021-06-04 Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels Vasseur, Sebastien Pérez Aznarte, José L. Sci Rep Article High concentration episodes for NO2 are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting, as oposed to point-forecasting, is a family of techniques that allow for the prediction of the expected distribution function instead of a single future value. In the case of NO(2), it allows for the calculation of future chances of exceeding thresholds and to detect pollution peaks. However, there is a lack of comparative studies for probabilistic models in the field of air pollution. In this work, we thoroughly compared 10 state of the art quantile regression models, using them to predict the distribution of NO(2) concentrations in a urban location for a set of forecasting horizons (up to 60 hours into the future). Instead of using directly the quantiles, we derived from them the parameters of a predicted distribution, rendering this method semi-parametric. Amongst the models tested, quantile gradient boosted trees show the best performance, yielding the best results for both expected point value and full distribution. However, we found the simpler quantile k-nearest neighbors combined with a linear regression provided similar results with much lower training time and complexity. Nature Publishing Group UK 2021-06-02 /pmc/articles/PMC8173015/ /pubmed/34078925 http://dx.doi.org/10.1038/s41598-021-90063-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vasseur, Sebastien Pérez Aznarte, José L. Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels |
title | Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels |
title_full | Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels |
title_fullStr | Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels |
title_full_unstemmed | Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels |
title_short | Comparing quantile regression methods for probabilistic forecasting of NO(2) pollution levels |
title_sort | comparing quantile regression methods for probabilistic forecasting of no(2) pollution levels |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173015/ https://www.ncbi.nlm.nih.gov/pubmed/34078925 http://dx.doi.org/10.1038/s41598-021-90063-3 |
work_keys_str_mv | AT vasseursebastienperez comparingquantileregressionmethodsforprobabilisticforecastingofno2pollutionlevels AT aznartejosel comparingquantileregressionmethodsforprobabilisticforecastingofno2pollutionlevels |