| 基于PSR-WSVM模型的边坡位移预测 |
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| 引用本文： | 李建新,刘小生,肖 钢,周 文,刘仁志.基于PSR-WSVM模型的边坡位移预测[J].大地测量与地球动力学,2020,40(6):577-580. |
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| 作者姓名： | 李建新 刘小生 肖 钢 周 文 刘仁志 |
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| 作者单位： | 江西理工大学建筑与测绘工程学院;河海大学地球科学与工程学院 |
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| 基金项目： | 国家自然科学基金(41561091)。 |
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| 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。
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| 关 键 词： | 相空间重构 小波核函数 支持向量机 边坡位移预测 |
Slope Displacement Prediction Based on PSR-WSVM Model |
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| LI Jianxin,LIU Xiaosheng,XIAO Gang,ZHOU Wen,LIU Renzhi.Slope Displacement Prediction Based on PSR-WSVM Model[J].Journal of Geodesy and Geodynamics,2020,40(6):577-580. |
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| Authors: | LI Jianxin LIU Xiaosheng XIAO Gang ZHOU Wen LIU Renzhi |
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| Institution: | (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) |
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| Abstract: | 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. |
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| Keywords: | phase space reconstruction wavelet kernel function support vector machine slope displacement prediction |
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