Comparison of Different Regularized and Shrinkage Regression Methods to Predict Daily Tropospheric Ozone Concentration in the Grand Casablanca Area

Abstract

Tropospheric ozone (O3) is one of the pollutants that have a significant impact on human health. It can increase the rate of asthma crises, cause permanent lung infections and death. Predicting its concentration levels is therefore important for planning atmospheric protection strategies. The aim of this study is to predict the daily mean O3 concentration one day ahead in the Grand Casablanca area of Morocco using primary pollutants and meteorological variables. Since the available explanatory variables are multicollinear, multiple linear regressions are likely to lead to unstable models. To counteract the multicollinearity problem, we compared several alternative regression methods: 1) Continuum Regression; 2) Ridge & Lasso Regressions; 3) Principal component regression (PCR); 4) Partial least Square regression & sparse PLS and; 5) Biased Power Regression. The aim is to set up a good prediction model of the daily ozone in the Grand Casablanca area. These models are fitted on a training data set (from the years 2013 and 2014), tested on a data set (from 2015) and validated on yet another data set data (from 2015). The Lasso model showed a better performance for the prediction of ozone concentrations compared to multiple linear regression and its other alternative methods.