基于灰色BP-NN优化组合的PM2.5预测  

PM2.5 prediction based on the optimal combination of grey BP neural network

作　　者：黄鹰[1] 史爱武[2] 陈占龙[1] 张威 Huang Ying;Shi Aiwu;Chen Zhanlong;Zhang Wei(School of Geography Information Engineering,China University of Geosciences,Wuhan 430070,China;School of Mathematics and Computer,Wuhan Textile University,Wuhan 430070,China)

机构地区：[1]中国地质大学(武汉)地理与信息工程学院,湖北武汉430070 [2]武汉纺织大学计算机学院,湖北武汉430070

出　　处：《电子技术应用》2020年第6期82-85,92,共5页Application of Electronic Technique

摘　　要：针对传统的BP神经网络模型无法有效表达时间序列数据中存在的历史特征的缺陷,提出利用灰色预测原理具备发现事物历史变化规律性的优势来解决BP神经网络预测模型的这一弱点,最后得到的灰色BP-NN优化组合模型具备了更高的预测精度。实验采用中国气象站2018年1月至2月北京市10个监测点的PM2.5质量浓度及其对应的每小时的空气污染物浓度、气象因子建立神经网络预测模型,并采用灰色预测算法对神经网络模型进行改进,改进后的结果为:在系统误差上有了较大的降低,同时预测结果与实测结果之间的拟合程度更好。Aiming at the defect that the traditional BP neural network model cannot effectively express the historical features existing in time series data,a method with the combination of BP neural network and grey forecast principle was proposed.Furthermore,grey forecast principle has the advantage of discovering the laws of historical changes,which can overcome the weakness of BP neural network prediction model and this method have higher prediction accuracy.The neural network prediction model was established by using the PM2.5 mass concentration of ten monitoring stations in Beijing in January and February 2018,as well as the corresponding hourly air pollutant concentration and meteorological factors.Meanwhile,the grey forecast algorithm was used to improve the neural network model.The results indicate that the improved method has the features of lower system error,and better fitting degree between the predicted result and the measured result.

关 键 词：空气污染 PM2.5浓度预测 气象因子 神经网络 灰色预测算法 时间序列数据 拟合 

分 类 号：TN711[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]