Improved pollution forecasting hybrid algorithms based on the ensemble method

Abstract

• The proposed model realizes the accurate multi-step forecasting for urban fine particle concentration. • The empirical wavelet transform and Stacking ensemble methods improve the accuracy of the proposed model. • The Hampel identifier and outlier robust extreme learning machine are used to realize the robustness of the forecasting. • The inverse empirical wavelet transform reconstruction solves the over-fitting problem of the forecasting. In a new fine particle concentrations forecasting model, the Hampel identifier outlier correction preprocessing detects and corrects the outliers in the original series. Empirical wavelet transform method decomposes the corrected series into a set of subseries adaptively, and each subseries are used to train the Stacking ensemble method. In the Stacking ensemble forecasting method, the outlier robust extreme learning machine meta-learner combines different Elman neural network base learners and outputs the forecasting results of different subseries. Different forecasting subseries are combined and then reconstructed by inverse empirical wavelet transform reconstruction method to get the final forecasting fine particle concentrations results. It has been proved in the study that the model proposed in the study has better accuracy and wide applicability comparing to the existing models.