PM_2.5 concentration forecasting: Development of integrated multivariate variational mode decomposition with kernel Ridge regression and weighted mean of vectors optimization

The accurate prediction of the PM_2.5 air quality parameter within industrial and urban settings is the most pressing issue examined by the researchers because it has high health implications. However, accurately forecasting PM_2.5 levels is crucial. The traditional machine learning (ML) models are incapable, as the indices fluctuate daily. To successfully manage this problem, a new ML framework is proposed that incorporates various techniques, such as LGBM feature selection (light gradient-boosting machine), MVMD (multivariate variational mode decomposition), KRidge (kernel Ridge regression), and INFO (weighted mean of vectors). The proposed framework is used to estimate PM_2.5 pollution at specific stations in China for a one-time prediction. The LGBM feature selection technique is the first step of the pre-processing, which performs to select the most important variables. Next, the MVMD splits the initial signal into intrinsic mode functions (IMFs), accommodating the signal's non-stationary multivariate. Following that, the KRidge approach is applied to each sub-component using the best input feature, and the resulting predictions are summed up to get the PM_2.5 levels. To assess the validity of the proposed MVMD-KRidge-INFO model, the categories of gaussian-process-regression (GPR), locally-weighted-linear-regression (LWLR), and multivariate adaptive regression splines (MARS) are analyzed in individual and hybrid moldes. As observed from the research results, MVMD-KRidge-INFO performs optimally at forecasting PM_2.5 levels at Huairou and Shunyi, as evidenced by R, RMSE, MAPE, I_A, MdAE, and U_95% metrics.