Prediction of daily maximum ozone levels using Lasso sparse modeling method
It addresses air quality forecasting for environmental monitoring, but is incremental as it applies an existing method to a specific domain.
This paper tackles the problem of predicting next-day maximum ozone levels using Lasso sparse modeling, achieving RMSE=5.63 ppb and MAE=4.42 ppb for maximum concentration, and RMSE=5.68 ppb and MAE=4.52 ppb for 8-hour-mean concentration, with the model outperforming other recent methods.
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.