Hybrid algorithm for short-term forecasting of PM2.5 in China

Abstract

In recent years, the forecasting of particles with a diameter of 2.5 μm or less (PM2.5) has been a popular research topic, and involves multiple sources of pollution, making it difficult to determine all of the contributing meteorological and environmental factors. When only the PM2.5 concentration time series is considered without other exogenous information, accurate forecasting is important and should be efficient. To address this problem, this paper proposes a hybrid algorithm consisting of multiple models to improve prediction accuracy. The innovation of the proposed hybrid algorithm is to decompose the original single one-dimensional (1D) PM2.5 data into multi-dimensional information which effectively mines information hidden in the 1D data. Then, it uses traditional prediction methods to forecast each sequence and to reconstruct its forecasting results to obtain the final forecasting results. Three hybrid models, Wavelet-ANN, Wavelet-ARIMA and Wavelet-SVM, are developed to forecast the 2016 PM2.5 trends in 5 Cities in China. The results showed that: (1) Hybrid models (Wavelet-ANN, Wavelet-ARIMA and Wavelet-SVM) can forecast short-term PM2.5 concentrations in China. Compared with the traditional Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) models, hybrid models can significantly improve prediction accuracy. (2) The Wavelet-ARIMA model has higher accuracy with respect to predicting PM2.5 concentrations. In particular, it can more accurately capture the mutational points of PM2.5 concentrations, which can provide effective information support for generating warnings about atmospheric pollution. The hybrid algorithm proposed in this paper can be effectively applied to the short-term forecasting of PM2.5 concentrations and can significantly improve the accuracy of prediction.