长江科学院院报2012，Vol.29Issue(4)：30-34,5.
基于RBF多变量时间序列的滑坡位移预测研究
Landslide Displacement Prediction by Using Multivariable Time Series Based on RBF Neural Network
曾耀 1李春峰1
作者信息
- 1. 贵州省交通规划勘察设计研究院股份有限公司,贵阳550001
- 折叠
摘要
Abstract
Slope is a chaotic dynamic system influenced by various factors. It' s difficult to establish the deterministic equation of slope displacement since it is highly uncertain as a macro expression of the internal mechanical behavior of slope. Landslide is a genetic type of slope which has the same characteristics. Apart from groundwater, the major external motivation of landslide displacement, it is under the control of remedial measures after its treatment. Chaotic time series of landslide displacement and its influential factors could reflect the history of landslide displacement. According to the observed multivariable time series and the mapping relation between variables reflected by adopting RBF neural network, the displacement is predicted by reconstructing the dynamic system of landslide displacement. Results show that multivariable time series model could effectively predict landslide displacement , and the accuracy is higher than that of single-variable time series model; multivariable time series model is of clearer sense of the physical mechanics and reflects the real characteristics of deformation evolution.关键词
滑坡预测/混沌/多变量时间序列/RBF神经网络Key words
prediction of landslide/chaos/multivariable time series/RBF neural network分类
天文与地球科学引用本文复制引用
曾耀,李春峰..基于RBF多变量时间序列的滑坡位移预测研究[J].长江科学院院报,2012,29(4):30-34,5.
