基于小波变换的组合模型在大坝变形分析中的应用  

Application of Combination Model Based on Wavelet Transform in Dam Deformation Analysis

作　　者：周朱迪 吴桂兰 ZHOU Zhudi;WU Guilan(Zhejiang Provincial Institute of Surveying and Mapping Science and Technology,Hangzhou 311100,China;Hangzhou Branch of Zhejiang Land Survey and Planning Co.,Ltd.,Hangzhou 310030,China)

机构地区：[1]浙江省测绘科学技术研究院,浙江杭州311100 [2]浙江省国土勘测规划有限公司杭州分公司,浙江杭州310030

出　　处：《测绘与空间地理信息》2024年第10期167-170,共4页Geomatics & Spatial Information Technology

摘　　要：传统的GM(1,1)模型在进行变形预测时受原始数据条件影响较大,预测精度受限。为了提高GM(1,1)模型的预测精度,更好地服务于大坝变形的趋势判断,本文在传统GM(1,1)模型的基础上引入数据去噪方法,构建组合预测模型,并将组合预测模型应用于实际大坝沉降变形监测数据预测中。实验结果表明,小波-卡尔曼滤波去噪方法去噪结果优于单一的小波去噪方法或卡尔曼滤波去噪方法的去噪结果,同时小波-卡尔曼滤波去噪方法去噪后数据的预测精度更高,更加适用于实际工程项目变形监测。The traditional GM(1,1)model is greatly affected by the original data conditions in deformation prediction,and the prediction accuracy is limited.In order to improve the prediction accuracy of GM(1,1)model and better serve the trend judgment of dam deformation,this paper introduces the data denoising method based on the traditional GM(1,1)model,constructs a combined prediction model,and applies the combined prediction model to the prediction of actual dam settlement deformation monitoring data.The experimental results show that the denoising results of wavelet Kalman filter denoising method is better than that of individual wavelet denoising method and individual Kalman filter denoising method.At the same time,the prediction accuracy of the denoised data of wavelet Kalman filter denoising method is higher,which is more suitable for the deformation monitoring of actual engineering projects.

关 键 词：小波变换 卡尔曼滤波 GM(1 1)模型 大坝 变形预测 

分 类 号：P25[天文地球—测绘科学与技术] TB22[天文地球—大地测量学与测量工程]