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

Abstract

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.

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

“…In this work, four machine learning (ML) models i.e. Lasso regression, Ridge regression [7], Support Vector Machine (SVM) [8,9] and Random Forest (RF) [10,11] are used to evaluate the prediction performance of chlorophyll concentration. In the process of model training, the data set is randomly divided into a training set and testing set at a ratio of 8:2, resulting 13287 training samples and 3322 testing samples.…”

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

“…To solve the above problems, it is necessary to find an analytical method that can overcome the problem of multicollinearity among the influencing factors. In previous studies, ridge regression, principal component regression, and partial least squares regression have achieved good results in overcoming the problem of multicollinearity [37][38][39][40]. Ridge regression is essentially an improved least squares estimation method.…”

“…It is more practical to obtain the regression coefficient at the cost of losing some information and reducing accuracy by abandoning the unbiased nature of the least squares method. Therefore, compared with principal component regression and partial least squares regression, the regression coefficient of ridge regression model is higher, but the accuracy is lower [40]. Both partial least squares regression and principal component regression achieve the purpose of data dimensionality reduction by extracting principal components, and then overcome the influence of multicollinearity.…”

Time series-based PM2.5 concentration prediction in Jing-Jin-Ji area using machine learning algorithm models

“…Traditional statistical models such as partial least squares regression model ( Polat and Gunay, 2015 ), generalized Markov model ( Sun et al., 2013 ; Alyousifi et al., 2019 ), Bayesian method ( Riccio et al., 2006 ; Liu et al., 2008 ; Faganeli Pucer et al., 2018 ), etc., are often used for the prediction of air pollutant concentration on time series. However, because these models all have the shortcoming of over-simplified, they inherently have difficulties in unraveling the nonlinear interaction relationship between multivariate factors and PM 2.5 concentration, so that the favorable factors for PM 2.5 prediction cannot be fully utilized ( Ni et al., 2017 ).…”