Application of LSTM and Prophet Algorithm in Slope Displacement Prediction

2021 ◽  
pp. 73-92
Author(s):  
Wengang Zhang ◽  
Yanmei Zhang ◽  
Xin Gu ◽  
Chongzhi Wu ◽  
Liang Han
Keyword(s):  
Displacement Prediction ◽  
Slope Displacement

Slope displacement prediction based on morphological filtering

2013 ◽  
Vol 20 (6) ◽  
pp. 1724-1730 ◽  
Author(s):  
Qi-yue Li ◽  
Jie Xu ◽  
Wei-hua Wang ◽  
Zuo-peng Fan
Keyword(s):  
Displacement Prediction ◽  
Morphological Filtering ◽  
Slope Displacement

Slope displacement prediction using sequential intelligent computing algorithms

Measurement ◽  
2019 ◽  
Vol 134 ◽  
pp. 634-648 ◽  
Author(s):  
Chengyin Liu ◽  
Zhaoshuo Jiang ◽  
Xishuang Han ◽  
Wanxi Zhou
Keyword(s):  
Displacement Prediction ◽  
Intelligent Computing ◽  
Slope Displacement

Slope Displacement Prediction Model Based on LMD and BP Neural Network

2012 ◽  
Vol 246-247 ◽  
pp. 370-376
Author(s):  
Wei Yu ◽  
Jing Lu Cai ◽  
Feng Ping An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
High Accuracy ◽  
Displacement Prediction ◽  
Local Mean ◽  
Slope Displacement

Slope displacement time series prediction model,a combination of Local mean decomposition(LMD) and BP neural network is presented.By selecting train samples on the basis of monitoring data on slope displacement and conducting an adaptive decomposing, several production function is obtained.After that, BP neural network is used to forecast the PF and finally adding it all up and the result is the predicton of slope displacement. BP neural network is used to optimize the parameters so as to improve the forecast accuracy.The model is put into application on the slope displacement forecasting of the permanent lock slope.The case study shows that the prediction result is of high accuracy, scientifically valid and has potential value in the field of slope displacement time series prediction.

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