反向累加生成绝对灰度性质及其在灾害事件预测中的应用

Nature of absolute grey degree generated by inverse accumulation and its application in disaster event prediction

为研究反向累加生成序列绝对灰度的变化规律,基于原始序列与其一次反向累加生成序列的级比关系,推导了整数阶反向累加生成序列绝对灰度的一般表达式及其相关性质。结合实际问题背景,讨论了融合绝对灰度的反向累加灰色模型在突发灾害事件预测中的应用效果。研究结果表明:当原始序列的绝对灰度大于0.6时,进行反向累加生成变换能够有效降低序列的绝对灰度,当原始序列的绝对灰度小于0.25时,对序列进行反向累加生成变换会增大序列的绝对灰度。研究结论为灰建模的模型选择及序列预处理方式的选择提供参考。

In order to study the variation of the absolute grey degree of reverse accumulation generates sequences,the general expression of the integer order reverse accumulation generates sequences absolute grey degree and its related properties are derived based on the class ratio relationship between the original sequence and its reverse accumulation generates sequences.The application effect of the backward-accumulative grey model incorporating absolute grey degree to the prediction of sudden-onset disasters is discussed in the context of practical problems.The results show that when the absolute grey degree of the original sequence is greater than 0.6,the reverse cumulative generation can effectively reduce the absolute grey degree of the sequence.When the absolute grey degree of the original sequence is less than 0.25,the reverse cumulative generation increases the absolute grey degree of the sequence.The findings of the study provide a reference for the model selection and data pre-processing of grey modeling in practical problems.