Dynamic landslide displacement prediction method based on long short term memory network (LSTM)

A technology of dynamic prediction and displacement, applied in neural learning methods, measuring devices, biological neural network models, etc., can solve problems such as gradient explosion and gradient disappearance, and achieve the effects of avoiding large deviations, increasing timeliness, and improving prediction accuracy

Inactive Publication Date: 2019-11-19
XI'AN POLYTECHNIC UNIVERSITY
View PDF0 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a dynamic prediction method of landslide displacement based on LSTM, which solves the phenomenon of gradient explosion and gradient disappearance that may occur during training of the existing RNN network model, thereby further improving the prediction accuracy of landslide displacement of the training model

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Dynamic landslide displacement prediction method based on long short term memory network (LSTM)
  • Dynamic landslide displacement prediction method based on long short term memory network (LSTM)
  • Dynamic landslide displacement prediction method based on long short term memory network (LSTM)

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0103] Using Matlab to construct LSTM dynamic prediction model of landslide displacement:

[0104] Data loading and normalization processing:

[0105] [train_data, test_data] = LSTM_data_process();

[0106] data_length=size(train_data,1);

[0107] data_num=size(train_data,2);

[0108] Node number setting after grid search method:

[0109] input_num=12;

[0110] cell_num=18;

[0111] output_num=4;

[0112] The bias of the gates in the network:

[0113] bias_input_gate = rand(1, cell_num);

[0114] bias_forget_gate = rand(1, cell_num);

[0115] bias_output_gate = rand(1, cell_num);

[0116] Network layer weight initialization:

[0117] ab=15.5;

[0118] weight_input_x = rand(input_num, cell_num) / ab;

[0119] weight_input_h=rand(output_num, cell_num) / ab;

[0120] weight_inputgate_x = rand(input_num, cell_num) / ab;

[0121] weight_inputgate_c = rand(cell_num, cell_num) / ab;

[0122] weight_forgetgate_x = rand(input_num, cell_num) / ab;

[0123] weight_forgetgate_c = ra...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a dynamic landslide displacement prediction method based on a long short term memory network (LSTM). The method comprises the steps of firstly building an online landslide displacement monitoring system, monitoring in real time to acquire complete displacement data within a period, removing abnormal values of the collected displacement data via a 3[omega] algorithm, and normalizing; then, building and training a landslide displacement prediction model of LSTM; and at last, using the acquired normalized data as an input of the model to be input into the landslide displacement prediction model, processing the input data via the prediction model, and thus achieving prediction on landslide displacement in a future period. According to the dynamic landslide displacementprediction method based on LSTM disclosed by the invention, the phenomena of gradient explosion and gradient vanishing that may appear when a recurrent neural network (RNN) network model is training are avoided, and thus the landslide displacement prediction accuracy of the training model is further improved.

Description

technical field [0001] The invention belongs to the technical field of geological disaster monitoring and forecasting, and in particular relates to a dynamic prediction method of landslide displacement based on a long short term memory network (Long Short Term Memory Network, LSTM). Background technique [0002] Landslide disasters are common geological disasters in China. Because of their wide distribution, frequent occurrence, and rapid movement, the economic losses caused by landslides can reach 1 billion US dollars every year. Not only that, but the secondary disasters caused by landslides are immeasurable. Therefore, it is of great economic value and social significance to take necessary measures to monitor it, and then to predict and forecast landslide disasters scientifically and effectively. [0003] In the previous studies on landslide displacement prediction, intelligent algorithms such as RBF (Radial Basis Function), BP (Back Propagation), and k-means were mainly...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01B21/02G06N3/04G06N3/08
CPCG01B21/02G06N3/084G06N3/044G06N3/045
Inventor 温宗周郭伏李丽敏陈鹏年陈曙东张顺锋刘德阳
Owner XI'AN POLYTECHNIC UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap