基于PSR-WSVM模型的边坡位移预测  

Slope Displacement Prediction Based on PSR-WSVM Model

作　　者：李建新 刘小生[1] 肖钢 周文 刘仁志 LI Jianxin;LIU Xiaosheng;XIAO Gang;ZHOU Wen;LIU Renzhi(School of Architecture and Surveying and Mapping Engineering,Jiangxi University of Science and Technology,86 Hongqi Road,Ganzhou 341000,China;School of Earth Sciences and Engineering,Hohai University,8 West-Focheng Road,Nanjing 211100,China)

机构地区：[1]江西理工大学建筑与测绘工程学院,江西省赣州市341000 [2]河海大学地球科学与工程学院,南京市211100

出　　处：《大地测量与地球动力学》2020年第6期577-580,共4页Journal of Geodesy and Geodynamics

基　　金：国家自然科学基金(41561091)。

摘　　要：为建立高精度的边坡位移预测模型,采用相空间重构(PSR)将边坡位移时间序列数据转换为多维数据,同时构造小波核函数改进的支持向量机模型,建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机(SVM)模型、小波支持向量机(WSVM)模型和基于相空间重构的支持向量机(PSR-SVM)模型预测结果进行对比,通过平均绝对误差(MAE)、平均绝对误差百分比(MAPE)和均方根误差(RMSE)3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明,PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型,边坡位移预测的精度明显提升。In order to establish a high-precision slope displacement prediction model,we use phase space reconstruction(PSR)to transform the slope displacement time series data into multi-dimensional data.The wavelet kernel function is constructed to improve the support vector machine model and to establish the PSR-WSVM model.The model is applied to slope displacement prediction.The PSR-WSVM model prediction results are compared with the traditional support vector machine model(SVM),wavelet support vector machine model(WSVM)and phase space reconstruction-based support vector machine model(PSR-SVM)prediction results.The average absolute error is passed.Mean absolute error percentage(MAPE)and root mean square error(RMSE)accuracy evaluation indicators verify the feasibility of the PSR-WSVM model.The results of engineering examples show that the three precision evaluation indexes of PSR-WSVM model prediction result are better than the other three models,and the accuracy of slope displacement prediction has obvious improvement.

关 键 词：相空间重构 小波核函数 支持向量机 边坡位移预测 

分 类 号：P258[天文地球—测绘科学与技术]