A Novel Hybrid Model for PM2.5 Concentration Forecasting Based on Secondary Decomposition Ensemble and Weight Combination Optimization

Abstract

Accurate and efficient forecast of PM2.5 concentration is the primary prerequisite for promoting urban green development and improving residents' well-being. In this study, a hybrid model based on secondary decomposition ensemble and weight combination optimization is presented to materialize exact PM2.5 concentration prediction. First, the empirical wavelet transform (EWT) is adopted to disassemble the primeval PM2.5 concentration sequence to get high and low-frequency components. Considering the intricacy of high-frequency components and the difficulty of direct prediction. Therefore, it is further decomposed into a collection of modes with significant discrepancies by adaptive variational mode decomposition (AVMD). Second, the prediction network and meteorological data are determined respectively according to Hurst exponent. Then support vector regression (SVR) model and bidirectional long short-term memory (BILSTM) network are used to model each sequence separately. In addition, the weights of each forecast network were optimized by improved sparrow search algorithm (ISSA) to correct decomposition errors. Finally, all prediction results were weighted and integrated to receive the ultimate prediction values. The test results show that whether it is 1-step prediction, 3-step prediction or 5-step prediction, the proposed model has the best prediction effect in Beijing, Handan and additional Shanghai cases.INDEX TERMS Adaptive variational mode decomposition (AVMD), hurst exponent, improved sparrow search algorithm (ISSA), PM2.5 concentration prediction, weighted combination model.

Forecasting the Exceedances of PM2.5 in an Urban Area

“…Over the past years, several approaches have emerged concerning the development of PM forecasting systems based on deterministic or statistical models on various time scales [27,28]. Machine learning approaches gained popularity in the last few years, and various forecasting algorithms, including artificial neural networks (ANNs), random forests (RFs), hidden Markov models (HMMs), and hybrid methods, have been developed to predict PM concentrations [29,30].…”