Prediction of Sliding Slope Displacement Based on Intelligent Algorithm

被引:0
作者
Pei Zuan
Yong Huang
机构
[1] Chengdu University of Technology,State Key Laboratory of Geo
[2] Xin Jiang Communications Construction Company,hazard Prevention and Geo
来源
Wireless Personal Communications | 2018年 / 102卷
关键词
Intelligent algorithm; Slippery slope; Displacement prediction; RBF; BP;
D O I
暂无
中图分类号
学科分类号
摘要
In order to predict the landslide disaster effectively, the prediction system of landslide disaster based on intelligent algorithm is constructed. Lianziya and Gushuwu are used as the research object. The applicability of RBF and BP algorithm in landslide deformation displacement prediction is analyzed and compared. Based on the cumulative displacement curve of the landslide deformation, the deformation of the landslide is divided into three main stages: initial deformation, uniform deformation and acceleration deformation. When the classical intelligent algorithm BP is used to predict the landslide deformation, the selection of network structure parameters has a great influence on the prediction results. By optimizing the parameters, the optimal network structure can be constructed, and better prediction accuracy can be obtained. The results show that the improved algorithms overcome the defects of the standard BP algorithm to some extent. The accuracy of prediction is improved. LMBP has the best prediction effect. The RBF has more advantages than the LM-BP algorithm for predicting landslide deformation, which is in good agreement with the landslide curve.
引用
收藏
页码:3141 / 3157
页数:16
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共 40 条
[1]  
Wang D(2013)Application of gray GM(2,1) model to prediction of landslide deformation Hydrogeology and Engineering Geology 40 121-125
[2]  
Huang X(2013)Deformation prediction of landslide based on improved back-propagation neural network Cognitive Computation 5 56-62
[3]  
Wang MD(2015)Deformation prediction of landslide based on functional network Neurocomputing 149 151-157
[4]  
Huangqiong C(2015)Ground-based multi-view photogrammetry for the monitoring of landslide deformation and erosion Geomorphology 231 130-145
[5]  
Zhigang Z(2015)Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data ISPRS Journal of Photogrammetry and Remote Sensing 105 128-140
[6]  
Chen J(2015)Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method Geomorphology 231 314-330
[7]  
Zeng Z(2015)Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis Remote Sensing of Environment 156 45-57
[8]  
Jiang P(2015)Deformation characteristics and failure mode of the Zhujiadian landslide in the Three Gorges Reservoir, China Bulletin of Engineering Geology and the Environment 74 1-12
[9]  
Stumpf A(2016)Functioning and precipitation-displacement modelling of rainfall-induced deep-seated landslides subject to creep deformation Landslides 13 653-670
[10]  
Malet JP(2013)A hybrid intelligent algorithm based short-term load forecasting approach International Journal of Electrical Power and Energy Systems 45 313-324