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中国管理科学

中国管理科学 ›› 2016, Vol. 24 ›› Issue (9): 140-146.doi: 10.16381/j.cnki.issn1003-207x.2016.09.017

• 论文 • 上一篇    下一篇

基于Mexican Wv-SVM的震灾人员存活量模型

黄星1, 袁明1, 王绍玉2   

  1. 1. 西南科技大学经济管理学院, 四川 绵阳 621010;
    2. 哈尔滨工业大学建筑学院, 黑龙江 哈尔滨 150001
  • 收稿日期:2014-09-21 修回日期:2015-07-04 出版日期:2016-09-20 发布日期:2016-09-30
  • 通讯作者: 黄星(1979-),男(汉族),四川南充人,西南科技大学经济管理学院,博士,讲师,研究方向:应急物流与供应链,E-mail:huangxing6213@126.com. E-mail:huangxing6213@126.com
  • 基金资助:

    教育部人文社会科学研究基金（16YJC630040）；省教育厅重点项目（15SA0034）

The Survival Amount Model Based on Mexican Wavelet Nuclear-SVM in Earthquake Disaster

HUANG Xing1, YUN Ming1, WANG Shao-yu2   

  1. 1. School of Economics Management, Southwest university of Sciences and Technology, Mianyang 621010;
    2. School of Architecture, Harbin Institute and Technology, Harbin 150001
  • Received:2014-09-21 Revised:2015-07-04 Online:2016-09-20 Published:2016-09-30

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摘要： 有效预测震灾人员的存活情况是紧急配置应急资源和提高救援效率的首要工作。为提高震灾人员存活预测的精度，本文首先依据区域灾害系统理论和现有研究成果提出震灾人员存活预测指标。其次，针对震灾人员存活量指标数据的小样本、高维度、非线性特征，考虑将支持向量机（Support Vector Machine，SVM）模型引入震灾人员存活量预测中，为有效降低SVM在高维空间中非线性分类的误差，采用Mexican母小波核函数替换满足Mercer内积条件的核函数，以改变常规核函数缩小偏差的局限性，提出用于预测震灾人员存活量的Mexican小波SVM（Mexican Wavelet-SVM， Mexican Wv-SVM）模型。数值算例表明：相比于标准SVM、BP神经网络，Mexican Wv-SVM模型具有预测精度好、训练速度快和运行稳定性好的特征，证明了模型的可靠和有效。

关键词: 震灾人员存活量, 预测模型, 支持向量机, 鲁棒损失函数

Abstract: The first work of distribution relief resource and improving the rescue efficiency is the survival amount prediction. The object of this paper is mainly to improve the prediction accuracy of the survival amount in earthquake disaster. First of all, the prediction indexes are put forward based on regional disaster theory and literatures. Secondly, the method of Support Vector Machine (SVM) model is introduced as the survival amount prediction in earthquake disaster to solve the index data of the small sample, high dimension and nonlinear characteristics. In this paper the model of the survival amount in earthquake disaster is put forward which replaced Mercer kernel function of inner product conditions to the Mexican mother Wavelet kernel function to effectively reduce the SVM classification of nonlinear error in high dimensional space and the limitations of conventional kernel function reducing the deviation. Finally, 53 groups of sample data are collected with the model test and these data came from the Chinese earthquake cases in 1989-2005. These sample data has the characteristics of small sample, nonlinear and high dimension that can be used to test the Mexican Wv-the SVM model. The numerical example shows Mexican Wv-the SVM model has high forecasting accuracy, fast training speed and running stability good characteristics to be compared with the standard SVM and BP neural network. In a word, the model is proved to be reliable and effective.

Key words: earthquake survival amount, prediction model, Support Vector Machine (SVM), robust loss function

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