北京大学学报（自然科学版）2019，Vol.55Issue(4)：615-625,11.DOI:10.13209/j.0479-8023.2019.041
基于集合经验模态分解和 BP 神经网络的北京市 PM2.5 预报研究
PM2.5 Forecast of Beijing Based on Ensemble Empirical Mode Decomposition and BP Neural Network
摘要
Abstract
A hybrid model with ensemble empirical mode decomposition (EEMD) and BP (Back-Propagation) neural network for next-day forecasting of PM2.5 concentration in Beijing is developed. The results show that the forecast accuracy of the hybrid model is higher than single BP model. The main error comes from the highest frequency component. The input variables of the hybrid model need to contain information about the output variables. The level of pollutant concentration in the early stage has great influence on the prediction result of the models.关键词
集合经验模态分解算法(EEMD)/ BP神经网络/ PM2.5预报Key words
ensemble empirical mode decomposition (EEMD)/ BP neural network/ PM2.5 forecast引用本文复制引用
任晓晨,邹思琳,唐娴,韦骏..基于集合经验模态分解和 BP 神经网络的北京市 PM2.5 预报研究[J].北京大学学报（自然科学版）,2019,55(4):615-625,11.基金项目
国家自然科学基金(41476008, 41576018)和广西壮族自治区特聘专家专项经费(2018B08)资助 （41476008, 41576018）
