TITLE:
Comparison of Different Regularized and Shrinkage Regression Methods to Predict Daily Tropospheric Ozone Concentration in the Grand Casablanca Area
AUTHORS:
Halima Oufdou, Lise Bellanger, Amal Bergam, Angélina El Ghaziri, Kenza Khomsi, El Mostafa Qannari
KEYWORDS:
Multiple Linear Regression, Multicollinearity, Penalized Regression, Statistical Forecasting, Tropospheric Ozone
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.8 No.10,
October
26,
2018
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.