摘要
为了解决传统GM(1,1)模型在进行沉降预测时受环境干扰较大以及拟合与预测精度较低等问题,提出将时间距离与相对误差相结合的一种模型预测方法。通过对时间序列值进行加权,并引入新陈代谢思想,建立加权动态GM(1,1)模型。以高速铁路沉降监测数据作为试验数据,分别使用加权动态GM(1,1)模型与传统GM(1,1)模型、加权GM(1,1)模型进行拟合预测。结果表明,相比于传统GM(1,1)模型和加权GM(1,1)模型,加权动态GM(1,1)模型的拟合预测数据与实测数据较为一致,拟合精度与预测精度更高,在高速铁路沉降监测中具有高效性与准确性,可以反映出高速铁路沉降变形规律。
In order to solve the problems that the traditional GM(1,1)model is disturbed by the environment badly and the accuracy of fitting and prediction is low during settlement prediction,a model prediction method is proposed by combining time distance and relative error.Though weighting the value of time series and introducing the idea of metabolism,a weighted dynamic GM(1,1)model is established.Taking the settlement monitoring data of high speed railways as the test data,the weighted dynamic GM(1,1)model,traditional GM(1,1)model and weighted GM(1,1)model are used for fitting and prediction respectively.The results show that compared with the traditional GM(1,1)model and the weighted GM(1,1)model,the fitting prediction data of the weighted dynamic GM(1,1)model is more consistent with the measured data,and the fitting accuracy and prediction accuracy are higher.It has high efficiency and accuracy in the settlement monitoring of high speed railway,and can reflect the settlement deformation law of high speed railway.
作者
潘慧
PAN Hui(Zhejiang Hangzhou Branch,Land Survey and Planning Co.,Ltd.,Hangzhou,Zhejiang,310030,China)
出处
《测绘标准化》
2022年第1期54-58,共5页
Standardization of Surveying and Mapping
