Exponential smoothing method based on wavelet transform for slope displacement prediction

被引:0
作者
Hu, Wei [1 ]
Yang, Xingguo [1 ]
Xu, Fugang [2 ]
Hao, Minghui [2 ]
机构
[1] Sichuan Univ, State Key Lab Hydraul & Mt Eng, Chengdu 610065, Peoples R China
[2] Sichuan Univ, Coll Water Resources & Hydropower, Chengdu, Peoples R China
来源
2012 2ND INTERNATIONAL CONFERENCE ON UNCERTAINTY REASONING AND KNOWLEDGE ENGINEERING (URKE) | 2012年
基金
中国国家自然科学基金;
关键词
exponential smoothing prediction; discrete stationary wavelet transform; slope displacement; forecasting; NEURAL-NETWORK; TIME-SERIES; MODAL PARAMETERS; IDENTIFICATION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
It has important significance in engineering to analyze rock slope's evolution rule and forecast its development trend based on the safety monitoring displacement data. The actual slope monitoring sequence is non-stationary time series containing a number of errors, therefore, firstly discrete stationary wavelet transform (DSWT) are used to denoising for monitoring data, then the reconstruction series are transformed into a stationary sequence by first-order difference, finally exponential smoothing method is used to prediction for the stationary differential sequence. The combination forecasting model is applied to high slope displacement prediction on the left bank of Jinping I Hydropower Station, the calculation results show that the combined model have higher forecast accuracy compared with other prediction methods, most of the relative errors of the prediction results are less than 5%, meeting engineering prediction requirements.
引用
收藏
页码:264 / 267
页数:4
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