Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting

摘要

The harm of air pollution to human health has received close attention, and many scholars have done research on the relationship between air pollution and human health. NO2 and SO2 are two important air pollutants, which are the two major sources for acid rain. Acid rain has potential harm to water, soil, plants, buildings and people’s health. So, it is significant to develop effective model for NO2 and SO2 forecasting and warning. However, it is not easy to get precise forecasting results for the irregular NO2 and SO2 series. This research focuses on modeling and prediction of the two major sources of acid rain, NO2 and SO2, and four cities in Central China region are selected as the test data. Specially, Central China region has the most severe regions with the acid rain in China for a long time. The proposed procedure is named as two-step-hybrid model, the detailed procedure of the proposed model can be summarized as three steps: First, the original NO2 (or SO2) sequence is decomposed into high-frequency and low-frequency sequences by the Complementary Ensemble Empirical Mode Decomposition (CEEMD); Second, Support Vector Regression (SVR) model combined the Cuckoo Search algorithm (CS) and Grey Wolf Optimizer algorithm (GWO) are employed to model the high-frequency and low frequency sequences, respectively; Third, forecasting data of low frequency and high frequency are summed as the final prediction results for NO2 (or SO2). In terms of model selection and assessment process, the proposed model and the other established models are compared by the forecasting error measures, such as MAE, MAPE and RMSE. The forecasting comparisons showed that the proposed two-step-hybrid model based on CEEMD, SVR, CS and GWO has higher forecasting precision compared with other forecasting models. Specially, the hybrid model CEEMD-CS-GWO-SVR, the low-frequency data using the SVR-CS and the high frequency data using SVR-GWO, is the best model for the prediction of NO2 and SO2 for the cities in Central China.