论文标题
使用拉索稀疏建模方法预测每日最大臭氧水平
Prediction of daily maximum ozone levels using Lasso sparse modeling method
论文作者
论文摘要
本文将现代统计方法应用于第二天的最大臭氧浓度以及第二天的最大8小时均值臭氧浓度。该模型使用了许多候选特征，包括当今的各种污染物的每小时浓度水平，以及当今观察结果的气象变量和未来的预测值。为了解决这样的超高维问题，应用了绝对最小收缩和选择操作员（LASSO）。 $ l_1 $此方法的性质使自动功能维度降低和最终的稀疏模型。由3年数据培训的模型表明了相对良好的预测准确性，RMSE = 5.63 ppb，MAE = 4.42 ppb，可预测下一天的最大$ O_3 $浓度，RMSE = 5.68 ppb，MAE = 4.52 ppb，以预测下一天的最大8小时$ _3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3 $ o_3。我们的建模方法还与最近在现场应用的其他几种方法进行了比较，并在预测准确性中证明了优越性。
This paper applies modern statistical methods in the prediction of the next-day maximum ozone concentration, as well as the maximum 8-hour-mean ozone concentration of the next day. The model uses a large number of candidate features, including the present day's hourly concentration level of various pollutants, as well as the meteorological variables of the present day's observation and the future day's forecast values. In order to solve such an ultra-high dimensional problem, the least absolute shrinkage and selection operator (Lasso) was applied. The $L_1$ nature of this methodology enables the automatic feature dimension reduction, and a resultant sparse model. The model trained by 3-years data demonstrates relatively good prediction accuracy, with RMSE= 5.63 ppb, MAE= 4.42 ppb for predicting the next-day's maximum $O_3$ concentration, and RMSE= 5.68 ppb, MAE= 4.52 ppb for predicting the next-day's maximum 8-hour-mean $O_3$ concentration. Our modeling approach is also compared with several other methods recently applied in the field and demonstrates superiority in the prediction accuracy.
