中国群死群伤火灾数据插补及快速损失评估研究
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摘要
火在人类发展的进程中扮演着及其重要的角色。它既推动社会发展人类进步,也给人类带来创伤。自改革开放以来,我国进入了经济高速发展阶段,社会各类生产生活活动频率大幅增加,同时社会财富迅速积累,这使得火灾起数和火灾造成的经济损失都呈现出了上升趋势。其中,造成较大人员伤亡的群死群伤火灾更引起了公众的广泛关注。群死群伤火灾,尤其是特别重大的群死群伤火灾,往往需要多部门协同处理,超出了常规管理方式的应对处置范畴。本文针对中国群死群伤火灾,研究其应急处置过程中亟待解决的数据插补和快速损失评估等相关内容,解决群死群伤火灾应急处置工作面临的核心问题,为其提供理论支撑和评估体系。
针对群死群伤火灾的数据缺失问题,提出了基于主成分回归的数值型数据插补方法和基于多重对应分析的语义型数据插补方法。为对两种方法进行考察,设计了不同缺失率和不同事故等级的两组实验,分别以均方根误差和正确率做为判断准则。将前者的实验结果与均值插补、众值插补、K均值聚类插补、模糊C均值聚类插补和K最近邻插补等5种常用的数值型数据插补方法进行比较,将后者的实验结果与众值插补、多项逻辑回归插补和径向基函数神经网络插补等3种常用的语义型数据插补方法进行了比较。结果表明,基于主成分回归的数值型数据插补方法在低缺失率和高事故等级情况下均有较好表现；基于多重对应分析的语义型数据插补方法在不同缺失率和不同事故等级情况下对场所和原因的插补效果优十其它参比方法。而在综合考虑不同属性缺失率和不同事故等级重要性的情况下,两种方法的表现均优于其它参比方法。
针对群死群伤火灾快速损失评估需求,基于网络分析法构建了从火灾造成的结果角度出发的结果指标和从火灾发生的情景角度出发的情景指标。前者考虑了死亡人数、受伤人数、过火面积和财产损失等4个属性,后者包括了时间、场所和原因3个属性集合的18个属性。计算了两种指标的指标值,利用结果指标从火灾造成的结果角度进行损失评估,并通过探求情景指标与结果指标之间的关系,利用情景指标从火灾发生的情景角度进行损失评估。通过比较网络分析法与多项逻辑回归、多层感知器神经网络和径向基函数神经网络等3种方法对不同等级事故的分级正确率,发现网络分析法对较高等级事故的分级正确率更高,考虑事故等级重要性的综合正确率更高,对不同等级事故的分级结果更保守。同时,较高等级案例的结果指标值和情景指标值间的相关关系也被定量揭示。基于结果指标和情景指标对群死群伤火灾造成的损失进行评估,需要的输入前期易获取,得到的输出保守可接受,能够满足快速损失评估的需要。
考虑对于社会脆弱性的两个维度,对于灾害的暴露程度和抵御恢复能力,从社会经济状况、人口状况以及基础设施和生命线等状况三个角度收集了38个基础指标。采用z-score函数将数据标准化,对经过再调整的数据采用主成分分析进行降维并提取7个主成分(解释了83.9%的数据方差),采用加权求和模型将7个主成分合成,最终得到社会脆弱性指标。利用得到的社会脆弱性指标,分析了中国2004年至2010年31个省级行政区的社会脆弱性时空分布特征。结果显示社会脆弱性呈现出东西部高北中南部低的空间特征,东西部地区相对较高的社会脆弱性呈现出不同的形成模式：东部(如山东、安徽、江苏、河南和江西等)源自于社会经济的不平衡发展,西部(如西藏、四川、云南、贵州、甘肃和青海等)源自于社会经济发展的相对落后；呈现出逐年降低相互趋近的时间特征,逐年降低具有较好的线性趋势,可以用于预测未来短期年份的社会脆弱性值,既规避了数据获取问题,又简化了计算过程。对社会脆弱性指标进行了敏感性分析,结果显示社会脆弱性指标对空间尺度的变化较为敏感,而对时间尺度的变化表现出一定的稳定性。
基于结果指标和社会脆弱性指标计算加和结果指标和标准化社会脆弱性指标,提出简单的区域快速损失评估方法。利用得到的区域损失指标对中国2004年至2010年31个省级行政区的综合损失情况进行了评估及分级。以4个加和结果指标为输出,以7个主成分得分为输入,进一步提出基于数据包络分析的区域快速损失评估方法,利用得到的综合技术效率对中国2004年至2010年31个省级行政区的综合损失情况进行了评估及分级。对两种方法的结果进行了比较,认为简单的区域快速损失评估方法,其结果主要受加和结果指标的影响,适用于需强调损失结果的情况；基于数据包络分析的区域快速损失评估方法规避了对损失结果和承灾体社会脆弱性两者内在关系的假设,能够处理多输入多输出,考虑因素更为全面,对因素间内在关系的处理更为合理,得到的结果更为保守,更能体现损失结果与社会脆弱性对承灾体的综合影响。
Fire plays an important role in the development of human being. It promotes the society development and human progress, but creates disasters as well. Since the reform and open, the economy of China develops rapidly, resulting in the rise of activity frequency of production and living, the accumulation of social wealth, and the increase of fire numbers and losses. Thereinto, the high casualty fires which cause huge number of deaths and injuries draw the public attention especially. The process of high casualty fires requires the cooperation of multiple departments, which is beyond the scope of general risk management. Focusing on the high casualty fires, the data imputation and rapid loss assessment are studied, in order to solve the essential problems of emergency management of high casualty fires and provide theoretical foundation and assessment system.
To tackle the data missing problem of high casualty fires, a principle component regression based numerical data imputation method and a multiple correspondence analysis based categorical data imputation method were proposed. Experiments of different missing rates and accident levels were designed and root mean squared error and correct rate were used to evaluate these2methods, respectively. The former method was compared with mean, mode, k-means cluster, fuzzy c-means cluster, and k-nearest neighbor imputation methods, and the latter method was compared with mode, multinomial logistic regression, and radial basis function network imputation methods. The results show that the former method performs better under low missing rate and high accident level, the latter method performs better for the attributes of place and cause under different missing rates and accident levels, and these two proposed methods perform the best when the missing rate and accident level are both under consideration.
To realize the rapid loss assessment for high casualty fires, a consequence index from the view of fire consequence and a situation index from the view of fire situation were constructed basing on the analytic network process. The fomer index includes the attributes of death, injury, burned area, and property loss, and the latter index includes18attributes of3clusters of time, place, and cause. Two indexes were calculated and the consequence index was used for loss assessment from the aspect of fire consequence, and the situation index was used for loss assessment from the aspect of fire situation basing on the relationship between the2indexes. The correct rates of classification for different accident levels were compared between the proposed method and multinomial logistic regression, multi-layer perceptron network and radial basis function network. It is indicated that the results of the proposed method has a higher correct rate and is more conservative. Basing on the consequence index and situation index, the rapid loss assessment is realized with easily acquired input and conservative acceptable output.
Considering the aspects of exposure and resistance&recovery,38basic indexes of status of socioeconomic, demographic, and infrastructure&lifeline were collected. Data were standardized with z-score function and adjusted. Seven principle components were extracted by principle component analysis, and the social vulnerability index was synthesized by weighted addictive model. By analyzing the social vulnerability index, the time and space distributions of social vulnerability were revealed. The results show that the social vulnerability is high for the east and west, and low for the north, middle, and south by space, and the formation model of the high social vulnaerabilty of east and west are different:it is resulted from the unbalanced development for the east (e.g. Shandong, Anhui, Jiangsu, Henan, and Jiangxi) and the slow development for the west (e.g. Xizang, Sichuan, Yunnan, Guizhou, Gansu, and Qinghai); is decreasing and approaching each other by time, and the good linear trend can be used for forcasting to tackle the data collection problem and simplify the calculation process. The sensitivity analysis shows that the social vulnerability index is sensitive to the change in space scale, and stable to the change in time scale.
The summation of consequence index and the standardization of social vulnerability index were calculated basing on the consequence index and social vulnerability index, and a simple area rapid loss assessment method was proposed. With the results of area loss index, the comprehensive loss status was assessed and graded for all provincial administrative region of China from2004to2010. Furthermore, a data envelopment analysis based area rapid loss assessment method was proposed basing on the4inputs of summation of consequence index and7outputs of7principle component scores. The assessment and grading were conducted as well. These two methods were compared and the results show that the former one is mainly affected by the results of summation of consequence index, and is believed to be suitable for assessments where the losses should be emphasized; the latter one avoid the assumption of the relationship between loss consequence and social vulnerability, is capable of handle multi inputs and outputs, assess from a more comprehensive aspect, give more sonservative results, and is more suitable and applicabel for assessments where both the losses and the social vulnerability should be considered evenly.
针对群死群伤火灾的数据缺失问题,提出了基于主成分回归的数值型数据插补方法和基于多重对应分析的语义型数据插补方法。为对两种方法进行考察,设计了不同缺失率和不同事故等级的两组实验,分别以均方根误差和正确率做为判断准则。将前者的实验结果与均值插补、众值插补、K均值聚类插补、模糊C均值聚类插补和K最近邻插补等5种常用的数值型数据插补方法进行比较,将后者的实验结果与众值插补、多项逻辑回归插补和径向基函数神经网络插补等3种常用的语义型数据插补方法进行了比较。结果表明,基于主成分回归的数值型数据插补方法在低缺失率和高事故等级情况下均有较好表现；基于多重对应分析的语义型数据插补方法在不同缺失率和不同事故等级情况下对场所和原因的插补效果优十其它参比方法。而在综合考虑不同属性缺失率和不同事故等级重要性的情况下,两种方法的表现均优于其它参比方法。
针对群死群伤火灾快速损失评估需求,基于网络分析法构建了从火灾造成的结果角度出发的结果指标和从火灾发生的情景角度出发的情景指标。前者考虑了死亡人数、受伤人数、过火面积和财产损失等4个属性,后者包括了时间、场所和原因3个属性集合的18个属性。计算了两种指标的指标值,利用结果指标从火灾造成的结果角度进行损失评估,并通过探求情景指标与结果指标之间的关系,利用情景指标从火灾发生的情景角度进行损失评估。通过比较网络分析法与多项逻辑回归、多层感知器神经网络和径向基函数神经网络等3种方法对不同等级事故的分级正确率,发现网络分析法对较高等级事故的分级正确率更高,考虑事故等级重要性的综合正确率更高,对不同等级事故的分级结果更保守。同时,较高等级案例的结果指标值和情景指标值间的相关关系也被定量揭示。基于结果指标和情景指标对群死群伤火灾造成的损失进行评估,需要的输入前期易获取,得到的输出保守可接受,能够满足快速损失评估的需要。
考虑对于社会脆弱性的两个维度,对于灾害的暴露程度和抵御恢复能力,从社会经济状况、人口状况以及基础设施和生命线等状况三个角度收集了38个基础指标。采用z-score函数将数据标准化,对经过再调整的数据采用主成分分析进行降维并提取7个主成分(解释了83.9%的数据方差),采用加权求和模型将7个主成分合成,最终得到社会脆弱性指标。利用得到的社会脆弱性指标,分析了中国2004年至2010年31个省级行政区的社会脆弱性时空分布特征。结果显示社会脆弱性呈现出东西部高北中南部低的空间特征,东西部地区相对较高的社会脆弱性呈现出不同的形成模式：东部(如山东、安徽、江苏、河南和江西等)源自于社会经济的不平衡发展,西部(如西藏、四川、云南、贵州、甘肃和青海等)源自于社会经济发展的相对落后；呈现出逐年降低相互趋近的时间特征,逐年降低具有较好的线性趋势,可以用于预测未来短期年份的社会脆弱性值,既规避了数据获取问题,又简化了计算过程。对社会脆弱性指标进行了敏感性分析,结果显示社会脆弱性指标对空间尺度的变化较为敏感,而对时间尺度的变化表现出一定的稳定性。
基于结果指标和社会脆弱性指标计算加和结果指标和标准化社会脆弱性指标,提出简单的区域快速损失评估方法。利用得到的区域损失指标对中国2004年至2010年31个省级行政区的综合损失情况进行了评估及分级。以4个加和结果指标为输出,以7个主成分得分为输入,进一步提出基于数据包络分析的区域快速损失评估方法,利用得到的综合技术效率对中国2004年至2010年31个省级行政区的综合损失情况进行了评估及分级。对两种方法的结果进行了比较,认为简单的区域快速损失评估方法,其结果主要受加和结果指标的影响,适用于需强调损失结果的情况；基于数据包络分析的区域快速损失评估方法规避了对损失结果和承灾体社会脆弱性两者内在关系的假设,能够处理多输入多输出,考虑因素更为全面,对因素间内在关系的处理更为合理,得到的结果更为保守,更能体现损失结果与社会脆弱性对承灾体的综合影响。
Fire plays an important role in the development of human being. It promotes the society development and human progress, but creates disasters as well. Since the reform and open, the economy of China develops rapidly, resulting in the rise of activity frequency of production and living, the accumulation of social wealth, and the increase of fire numbers and losses. Thereinto, the high casualty fires which cause huge number of deaths and injuries draw the public attention especially. The process of high casualty fires requires the cooperation of multiple departments, which is beyond the scope of general risk management. Focusing on the high casualty fires, the data imputation and rapid loss assessment are studied, in order to solve the essential problems of emergency management of high casualty fires and provide theoretical foundation and assessment system.
To tackle the data missing problem of high casualty fires, a principle component regression based numerical data imputation method and a multiple correspondence analysis based categorical data imputation method were proposed. Experiments of different missing rates and accident levels were designed and root mean squared error and correct rate were used to evaluate these2methods, respectively. The former method was compared with mean, mode, k-means cluster, fuzzy c-means cluster, and k-nearest neighbor imputation methods, and the latter method was compared with mode, multinomial logistic regression, and radial basis function network imputation methods. The results show that the former method performs better under low missing rate and high accident level, the latter method performs better for the attributes of place and cause under different missing rates and accident levels, and these two proposed methods perform the best when the missing rate and accident level are both under consideration.
To realize the rapid loss assessment for high casualty fires, a consequence index from the view of fire consequence and a situation index from the view of fire situation were constructed basing on the analytic network process. The fomer index includes the attributes of death, injury, burned area, and property loss, and the latter index includes18attributes of3clusters of time, place, and cause. Two indexes were calculated and the consequence index was used for loss assessment from the aspect of fire consequence, and the situation index was used for loss assessment from the aspect of fire situation basing on the relationship between the2indexes. The correct rates of classification for different accident levels were compared between the proposed method and multinomial logistic regression, multi-layer perceptron network and radial basis function network. It is indicated that the results of the proposed method has a higher correct rate and is more conservative. Basing on the consequence index and situation index, the rapid loss assessment is realized with easily acquired input and conservative acceptable output.
Considering the aspects of exposure and resistance&recovery,38basic indexes of status of socioeconomic, demographic, and infrastructure&lifeline were collected. Data were standardized with z-score function and adjusted. Seven principle components were extracted by principle component analysis, and the social vulnerability index was synthesized by weighted addictive model. By analyzing the social vulnerability index, the time and space distributions of social vulnerability were revealed. The results show that the social vulnerability is high for the east and west, and low for the north, middle, and south by space, and the formation model of the high social vulnaerabilty of east and west are different:it is resulted from the unbalanced development for the east (e.g. Shandong, Anhui, Jiangsu, Henan, and Jiangxi) and the slow development for the west (e.g. Xizang, Sichuan, Yunnan, Guizhou, Gansu, and Qinghai); is decreasing and approaching each other by time, and the good linear trend can be used for forcasting to tackle the data collection problem and simplify the calculation process. The sensitivity analysis shows that the social vulnerability index is sensitive to the change in space scale, and stable to the change in time scale.
The summation of consequence index and the standardization of social vulnerability index were calculated basing on the consequence index and social vulnerability index, and a simple area rapid loss assessment method was proposed. With the results of area loss index, the comprehensive loss status was assessed and graded for all provincial administrative region of China from2004to2010. Furthermore, a data envelopment analysis based area rapid loss assessment method was proposed basing on the4inputs of summation of consequence index and7outputs of7principle component scores. The assessment and grading were conducted as well. These two methods were compared and the results show that the former one is mainly affected by the results of summation of consequence index, and is believed to be suitable for assessments where the losses should be emphasized; the latter one avoid the assumption of the relationship between loss consequence and social vulnerability, is capable of handle multi inputs and outputs, assess from a more comprehensive aspect, give more sonservative results, and is more suitable and applicabel for assessments where both the losses and the social vulnerability should be considered evenly.
引文
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