论文标题
极端否$ _2 $情节的概率预测方法：模型的比较
Probabilistic forecasting approaches for extreme NO$_2$ episodes: a comparison of models
论文作者
论文摘要
当局通过交通限制越来越多地处理了$ _2 $的高浓度发作，当空气质量恶化超过某些阈值时，这些限制会被激活。因此，预见到污染物浓度达到这些阈值的可能性成为必要。概率预测是一系列技术，可以预测预期分布函数而不是单个值。在没有$ _2 $的情况下，它允许计算超过阈值并检测污染峰的未来机会。我们彻底比较了10个最先进的概率预测模型，使用它们来预测一组预测的视野（长达60小时），在城市位置预测NO $ _2 $浓度的分布。分位数梯度增压树显示出最佳性能，可为预期值和预测完全分布带来最佳的结果。此外，我们展示了如何使用这种方法来检测污染峰。
High concentration episodes for NO$_2$ 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 is a family of techniques that allow for the prediction of the expected distribution function instead of a single value. In the case of NO$_2$, it allows for the calculation of future chances of exceeding thresholds and to detect pollution peaks. We thoroughly compared 10 state of the art probabilistic predictive 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). Quantile gradient boosted trees shows the best performance, yielding the best results for both the expected value and the forecast full distribution. Furthermore, we show how this approach can be used to detect pollution peaks.
