A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

气象参数对基于BP神经网络的PM_(2.5)日均值预报模型的影响

建立了基于BP神经网络的PM_(2.5)质量浓度预报模型,对广州市5个监测点2012年6月—2013年5月的PM_(2.5)质量浓度日均值进行预报,分析了总体预报误差、不同风速和降雨量下的预报误差,以及天气预报误差对PM_(2.5)质量浓度预报误差的影响。结果表明,BP神经网络模型对5个站点的PM_(2.5)预报结果稳定,平均相对误差为29.71%。在有利于PM_(2.5)扩散的气象条件下预报误差较大,风速较大时与风速较小时预报误差的差异高达15%,而不同降雨量情况下的预报误差较相近。修正天气预报后,各站点的预报误差平均降低了4.67%。这表明可从空气质量数据质量等方面入手改进模型。