多源信息集结对提高自然灾害环境下统计数据可信度具有重要作用,但信息渠道的多源性极易导致集结信息数据类型不一致、不兼容,形成灰色异构数据序列。本文应用灰色系统建模技术对灰色异构数据预测建模方法展开研究,首先,基于"核"和"灰度"对灰色异构数据进行规范化处理;然后,建立灰色异构数据"核"序列的DGM(1,1)模型,并以"核"为基础,根据灰度不减公理,以灰色异构数据序列中最大灰度值所对应的信息域作为预测结果之信息域,推导并构建了灰色异构数据预测模型;最后,将该模型应用于某地震帐篷需求量的预测。本文研究成果将传统灰色模拟及预测模型建模对象从"同质数据"拓展至"异构数据",对丰富与完善灰色模拟及预测模型理论体系,提高自然灾害救援效率具有积极意义。
Multiple-source information aggregation is a significant way to improve the reliability of the statistical data in the environment of natural disasters. However, the diversity of information sources often cause the inconformity and incompatible types of the aggregation information data which thereby produce isomerism data sequences that belong to different types of data. The modeling technology of grey system is applied to study the modeling method for grey isomerism data. First, the grey heterogeneous data is normalized based on the "Kernel" and "degree of greyness"; then a (1,1) DGM model of grey heterogeneous data "Kernel" sequence is established. According to the "Kernel" and the axiom of grey degree not-reducing, a grey prediction model of heterogeneous data is built by using the information field to which the maximum degree of greyness in the grey heterogeneous data sequence corresponding as the information field of the prediction results, Finally, the model is applied to forecast the demand volume of tent in the ya'an earthquake. The research results of the project will expand the traditional modeling objects of gray forecasting model from homogeneous data to heterogeneous data and has a vital significance for enriching and perfecting the grey prediction model theory system, and improving the efficiency of natural disasters and rescue.
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