BP神经网络模型在应急需求预测中的应用——以地震伤亡人数预测为例
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摘要
为更准确地预测应急需求,以地震伤亡人数预测为例,收集1990—2010年全国范围内破坏性地震的伤亡人数资料,综合考虑影响地震人员伤亡的众多因素,选取地震发生时间、震级、震中烈度、人口密度、抗震设防烈度、预报水平等6项主要因素作为评价指标,采用BP神经网络,以主成分作为输入层神经元,伤亡人数作为输出层神经元,经过网络训练对样本数据进行仿真,建立地震伤亡人数预测模型。算例表明,与高斯拟合函数模型相比,BP神经网络模型对地震后伤亡人数的预测精度提高了7.5%。
Earthquake casualty forecasting was improved with the model of BP neural network analysis.The data on earthquake casualties in China from 1990 to 2010 were collected as samples.Based on these data samples,some factors are selected as the main indicators,such as time of earthquake,earthquake magnitude,epicentral intensity,population density,resistance level and prediction level.First using the method of PCA to identify the principal components,then applying the model of BP neural network analysis to forecast the earthquake casualty.Finally,the proposed model and another forecasting model are used to predict the casualties in Yiliang earthquake(2012).The obtained result illustrates that the prediction accuracy is improved by 7.5%.
Earthquake casualty forecasting was improved with the model of BP neural network analysis.The data on earthquake casualties in China from 1990 to 2010 were collected as samples.Based on these data samples,some factors are selected as the main indicators,such as time of earthquake,earthquake magnitude,epicentral intensity,population density,resistance level and prediction level.First using the method of PCA to identify the principal components,then applying the model of BP neural network analysis to forecast the earthquake casualty.Finally,the proposed model and another forecasting model are used to predict the casualties in Yiliang earthquake(2012).The obtained result illustrates that the prediction accuracy is improved by 7.5%.
引文
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[3]刘金龙,林均岐.基于震中烈度的地震人员伤亡评估方法研究[J].自然灾害学报,2012,21(5):113-119.LIU Jin-long,LIN Jun-qi.Study on assessment method for earthquake casualty based on evidential intensity[J].Journal ofNatural Disasters,2012,21(5):113-119.
[4]张洁,高惠瑛,刘琦.基于汶川地震的地震人员伤亡预测模型研究[J].中国安全科学学报,2011,21(3):59-64.ZHANG Jie,GAO Hui-ying,LIU Qi.Study on earthquake casualty forecasting model based on Wenchuan earthquake[J].China Safety Science Journal,2011,21(3):59-64.
[5]施伟华,陈坤华,谢英情,等.云南地震灾害人员伤亡预测方法研究[J].地震研究,2012,35(3):387-392.SHI Wei-hua,CHEN Kun-hua,XIE Ying-qing,et al.Prediction method research on casualties due to earthquake disasterin Yunnan[J].Journal of Seismological Research,2012,35(3):387-392.
[6]何明哲,周文松.基于地震损伤指数的地震人员伤亡预测方法[J].哈尔滨工业大学学报,2011,43(4):23-27.HE Ming-zhe,ZHOU Wen-song.Prediction of seismic casualties based on the seismic damage index[J].Journal ofHarbin Institute of Technology,2011,43(4):23-27.
[7]高惠瑛,李清霞.地震人员伤亡快速评估模型研究[J].灾害学,2010,25(增):275-277.GAO Hui-ying,LI Qing-xia.Study on the rapid evaluation model for seismic casualties[J].Journal of Catastrophology,2010,25(S0):275-277.
[8]Erdik M,Sesetyan K,Demircioglu M B,et al.Rapid earthquake loss assessment after damaging earthquakes[J].SoilDynamics and Earthquake Engineering,2010,31(2):247-266.
[9]赵小柠,马昌喜.基于范例推理的灾害性地震应急物资需求预测研究[J].中国安全科学学报,2012,22(8):3-9.ZHAO Xiao-ning,MA Chang-xi.Research on predicting emergency material demand after disastrous earthquake based oncase-based reasoning[J].China Safety Science Journal,2012,22(8):3-9.
[10]梅长林,周家良.实用统计方法[M].北京:科学出版社,2002:53-57.MEI Chang-lin,ZHOU Jia-liang.Practical Statistical Methods[M].Beijing:Science Press,2002:53-57.
[11]张肇诚,郑大林,徐京华.中国震例(1989—1991)[M].北京:地震出版社,2000:147-169.ZHANG Zhao-cheng,ZHENG Da-lin,XU Jing-hua.Earthquake Cases in China(1989-1991)[M].Beijing:Earth-quake Press,2000.
[12]张肇诚,郑大林,车时.中国震例(1995—1996)[M].北京:地震出版社,2002:186-203.ZHANG Zhao-cheng,ZHENG Da-lin,CHE Shi.Earthquake Cases in China(1995-1996)[M].Beijing:EarthquakePress,2000:186-203.
[13]陈棋福,郑大林,高荣胜.中国震例(1997—1999)[M].北京:地震出版社,2002:349-381.CHEN Qi-fu,ZHENG Da-lin,GAO Rong-sheng.Earthquake Cases in China(1997-1999)[M].Beijing:EarthquakePress,2002:349-381.
[14]杨帆,郑宝柱,剡亮亮.基于BP神经网络的地震伤亡人数评估体系研究[J].震灾防御技术,2009,4(4):428-435.YANG Fan,ZHENG Bao-zhu,YAN Liang-liang.System of earthquake casualty assessment based on BP neuralnetwork[J].Technology for Earthquake Disaster Prevention,2009,4(4):428-435.
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