基于粗糙集与支持向量机的煤矿灾害应急物流能力评价  

Capability Evaluation of Coal Mine Disaster Emergency Logistics Based on Rough Set and Support Vector Machine

作　　者：王金凤[1] 盛旭阳[1] 翟雪琪[1] 冯立杰[1,2] 

机构地区：[1]郑州大学管理工程学院,河南郑州450001 [2]河南省煤层气开发利用有限公司,河南郑州450016

出　　处：《煤矿安全》2014年第11期237-239,共3页Safety in Coal Mines

基　　金：国家自然科学基金资助项目(71271194);河南省教育厅科学技术研究重点基金资助项目(13A630438)

摘　　要：提高应急物流能力是煤炭生产企业亟待解决的现实问题。提出采用粗糙集与支持向量机相结合的方法,在构建模型基础上评价煤矿的应急物流能力。首先,结合煤矿灾害特征,从组织协调、决策制定、物流运作供应能力、信息处理能力及救援队伍等方面构建评价指标体系;然后,在粗糙集与支持向量机理论框架下对评价指标进行约简,提出基于RS-SVM的评价模型;最后,通过实证研究验证模型的适用性。结果表明与直接采用支持向量机方法相比,最后提出的评价模型精确度可由98.6%提高至100%。The improvement of emergency logistics capability is the real issue that should be resolved by numerous coal mine enterpri- ses. This article proposed the method about combining the rough set （RS） with support vector machine （SVM） and evaluates the coal mine disaster emergency logistics on the base of establishing the capability evaluation model. First of all, by combing the characteristic of coal mine disaster, the evaluation index system could be established from some aspects, such as the organization and coordination, decision making, supply capacity of logistics operation, information processing ability and rescue teams. Then, on the basis of RS - SVM theoretical fundamental, the evaluation model of RS - SVM will be presented after the evaluation indexes reduction. Finally, the model application can be tested and verified by the empirical study. The result indicates that the accuracy of evaluation model in this article will be improved from 98.6% to 100% by comparison with applying the SVM directly.

关 键 词：煤矿灾害 应急物流 能力评价 RS-SVM评价模型 粗糙集 支持向量机 

分 类 号：TD79[矿业工程—矿井通风与安全]