新信息优先的灰色模型在沉降预测中的应用  

Application of Gray Model with New Information Priority in Settlement Prediction

作　　者：李浩飞 常伟纲 楚宪亮 LI Haofeil;CHANG Weigang;CHU Xianiang(Shandong University of Science and Technology Surveying and Mapping Science and Engineering College,Qingdao 266590,China)

机构地区：[1]山东科技大学测绘科学与工程学院,山东青岛266590

出　　处：《测绘地理信息》2020年第6期97-99,104,共4页Journal of Geomatics

基　　金：山东省重点研发项目(2017GSF220010)。

摘　　要：基于大量沉降监测序列监测后期趋稳的特点,利用新信息优先的新陈代谢灰色GM(1,1)模型和优化的反向累加灰色GOM(1,1)模型对工程中呈递减趋势的沉降序列进行建模和预测,并对不同建模原理得到的模拟和预测结果进行比较,最后将两种模型结合进行分析,结果表明:灰色GOM(1,1)模型对沉降序列有更好的预测效果,两种模型结合的新陈代谢GOM(1,1)模型的预测及模拟精度进一步的提高。Based on the tendency to stability of a large number of sedimentation sequences at the later stage of monitoring process,the metabolic GM(1,1) model and the optimized reverse additive GOM(1,1) model with new information priority were used in this paper to establish model and predict the descending trend of settlement sequences in engineering. Different modeling principles on simulation and prediction are compared and the results show that GOM(1,1) model improves the effect of prediction of the settlement sequence better, and the prediction accuracy is further improved after using the metabolic GOM(1,1) model.

关 键 词：新陈代谢GM(1 1) GOM(1 1)模型 沉降预测 误差分析 

分 类 号：P258[天文地球—测绘科学与技术] P207