BP人工神经网络模型在上海市感染性腹泻日发病例数预测中的应用

黎健; 顾君忠; 毛盛华; 肖文佳; 金汇明; 郑雅旭; 王永明; 胡家瑜

Abstract

黎健,顾君忠,毛盛华,肖文佳,金汇明,郑雅旭,王永明,胡家瑜.BP人工神经网络模型在上海市感染性腹泻日发病例数预测中的应用[J].Chinese journal of Epidemiology,2013,34(12):1198-1202

BP人工神经网络模型在上海市感染性腹泻日发病例数预测中的应用

Preliminary application of Back-Propagation artificial neural network model on the predictionof infectious diarrhea incidence in Shanghai

Received:July 15, 2013

DOI：10.3760/cmaj.issn.0254-6450.2013.012.010

KeyWord: 感染性腹泻气象因素 BP人工神经网络

English Key Word: Infectious diarrhea Meteorological factors Back-Propagation artificial neuralnetwork

FundProject:上海市公共卫生重点学科计划(12GWZX0101)

Author Name	Affiliation	E-mail
Li Jian	Shanghai Municipal Centerfor Disease Control and Prevention, Shanghai 200336, Ch1na
Gu Junzhong	Institute of Computer,Application, East Chirza Normal University
Mao Shenghua	Shanghai Municipal Centerfor Disease Control and Prevention, Shanghai 200336, Ch1na
Xiao Wenjia	Shanghai Municipal Centerfor Disease Control and Prevention, Shanghai 200336, Ch1na
Jinhui Ming	Shanghai Municipal Centerfor Disease Control and Prevention, Shanghai 200336, Ch1na
Zheng Yaxu	Shanghai Municipal Centerfor Disease Control and Prevention, Shanghai 200336, Ch1na
Wang Yongming	Institute of Computer,Application, East Chirza Normal University	ymwang@ica.stc.sh.cn
Hujia Yu	Shanghai Municipal Centerfor Disease Control and Prevention, Shanghai 200336, Ch1na	jyhu@scdc.sh.cn

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Abstract:

目的建立基于气象因素的上海市感染性腹泻逐H发病例数BP人工神经网络预测模型。方法收集上海市2005-2008年感染性腹泻逐日发病例数与同期气象资料包括气温、相对湿度、降雨量、气压、日照时数、风速,通过SPearman相关分析选出与感染性腹泻相关的气象因素,用主成分分析(PCA)去除气象因素间的共线性影响。利用MatLabR2012b软件的神经网络工具箱建立感染性腹泻日发病例数的BP神经网络预测模型,并对拟合效果、外推预测能力和等级预报效果进行评价。结果 SPearman相关性分析显示,日感染性腹泻与前两天的日最高气温、最低气温、平均气温、最低相对湿度、平均相对湿度呈正相关(PP<0.01)。输入PCA提取的4个气象主成分构建BP神经网络预测模型,训练和预测样本平均绝对误差、均方根误差、相关系数、决定系数分别为4.7811、6.8921、0.7918、0.8418和5.8163、7.8062、0。7202、0.8180。模型预测值对2008年实际发病数的年平均误差率为5,30%,对感染性腹泻的等级预报正确率为95.63%+H26。结论温度和气压对感染性腹泻日发病例数影响较大。BP神经网络模型的拟合及预测误差较小,预报正确率较高,预报效果理想。

English Abstract:

Objective To establish BP artificial neural network predicting model regardingthe daily cases of infectious diarrhea in Shanghai.Methods Data regarding both the incidence oinfectious diarrhea from 2005 to 2008 in Shanghai and meteorological factors including temperature.relative humidity.rainfall，atmospheric pressure.duration of sunshine and wind speed within the sameperiods were collected and analyzed with the MatLab R20 l 2b software.Meteorological factors thatwere correlated with infectious diarrhea were screened by Spearman correlation analysis.Principalcomponent analysis(PCA)was used to remove the multi-colinearities between meteorological factors.Back-Propagation(BP)neural network was employed to establish related prediction models regardingthe daily infectious diarrhea incidence.using artificial neural networks toolbox.The establishedmodels were evaluated through the fitting，predicting and forecasting processesResults Data fromSpearman correlation analysis indicated that the incidence of infectious diarrhea had a highly positivecorrelation with factors as daily maximum temperature，minimum temperature，average temperature，minimum relative humidity and average relative humidity in the previous two days(P<0.01)，and arelatively high negative correlation with the daily average air pressure in the previous two days(P<0.0 1).Factors as mean absolute error,root mean square error，correlation coemcient(r)，and thecoefficient of determination(r²)of BP neural network model were established under the input of 4meteorological principal components.extracted by PCA and used for training and prediction.Thenappeared to be 4.7811，6.8921，0.7918，0.8418 and 5.8163，7.8062，0.7202，0.8180，respectively.Therate on mean error regarding the predictive value to actual incidence in 2008 was 5.30%and theforecasting precision reached 95.63%.Conclusion Temperature and air pressure showed importantimpact on the incidence of infectious diarrhea.The BP neural network model had the advantages of10W simulation forecasting errors and high forecasting hit rate that could ideally predict and forecastthe effects OH the incidence of infectious diarrhea.

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