The Comparison of Partial Least Squares Regression, Principal Component Regression and Ridge Regression with Multiple Linear Regression for Predicting PM10 Concentration Level based on Meteorological Parameters

Esra Polat ； Suleyman Gunay

《Journal of Data Science》 13卷4期 (2015/10) Pp. 663-692

https://doi.org/10.6339/JDS.201510_13(4).0003

並列摘要

Air pollution shows itself as a serious problem in big cities in Turkey, especially for winter seasons. Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. Ambient PM10 (i.e particulate diameter less than 10um in size) pollution has negative impacts on human health and it is influenced by meteorological conditions. In this study, partial least squares regression, principal component regression, ridge regression and multiple linear regression methods are compared in modeling and predicting daily mean PM10 concentrations on the base of various meteorological parameters obtained for the city of Ankara, in Turkey. The analysed period is February 2007. The results show that while multiple linear regression and ridge regression yield somewhat better results for fitting to this dataset, principal component regression and partial least squares regression are better than both of them in terms of prediction of PM10 values for future datasets. In addition, partial least squares regression is the remarkable method in terms of predictive ability as it has a close performance with principal component regression even with less number of factors.

並列關鍵字

air pollution ； meteorological parameters ； model fit ； multicollinearity ； multiple linear regression ； principal component regression ； partial least squares regression ； ridge regression ； particulate matter (PM10) ； prediction