利用BP神经网络提高奥运会空气质量实时预报系统预报效果  

Update the Ensemble Air Quality Modeling System with BP Model during Beijing Olympics

作　　者：张伟[1,2] 王自发[1,3] 安俊岭[1] 杨婷[2,3] 唐晓[1,2,3] 

机构地区：[1]中国科学院大气物理研究所大气边界层与大气化学国家重点实验室,北京100029 [2]中国科学院研究生院,北京100049 [3]中国科学院大气物理研究所竺可桢-南森国际研究中心,北京100029

出　　处：《气候与环境研究》2010年第5期595-601,共7页Climatic and Environmental Research

基　　金：北京市科委重大项目"北京及近周边区域大气复合污染形成机制及防控措施研究示范"课题"区域大气污染模拟;预测;预警与示范"

摘　　要：将BP(Back Propagation)神经网络方法引入到奥运空气质量预报工作中,利用MATLAB神经网络工具箱搭建运行平台,将高时效性的观测结果与多模式集成实时预报系统的模式输出结果相结合,做出BP神经网络拟合预报结果。在对北京大学医学部站点2008年7月7日到8月26日模式模拟结果、观测结果以及BP神经网络拟合结果的对比研究中发现:BP神经网络能大大提高模式预报效果,平均误差率减少34.7%,相关系数提高39%,特别是在模式模拟效果较差的情况下,对提高预报效果更明显。对BP神经网络样本问题进行敏感性实验结果表明,样本数目多少并不是决定拟合效果的决定性因素,应选取具有稳定映射关系的样本,才是提高拟合预报效果的关键。Back Propagation neural network model（BP model） was applied to the Olympic air quality forecasting.By using MATLAB neural network toolbox,assembled air quality modeling forecasting system was combined with nearly real-time observations to fit BP neural network forecasting results.The measurements were made from 7 July to 26 August 2008 in the Peking University Health Science Center.Using these data,the performance of the BP neural network results is assessed.The results show that BP neural network can significantly improve the simulation results,with the average error rate decreasing by 34.7% and the correlation coefficient increasing by 39%.The advantage of BP neural network is more obvious when the original simulation results are poor.The sensitivity experiment of BP neural network sample size indicates that the number of samples is not the decisive factor in deciding fitting effect,and the stability of mapping relationship in samples plays a crucial role in raising the effect of prediction.

关 键 词：BP神经网络 空气质量预报 北京奥运会 

分 类 号：P401[天文地球—大气物理学与大气环境]