Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A Dynamic Gray Fairhurst Neural Network Landslide Deformation Prediction Method

A technology of neural network and prediction method, which is applied in the field of dynamic gray Fairhurst neural network landslide deformation prediction, can solve the problems of insufficient prediction accuracy and large error of prediction results, so as to improve prediction accuracy, improve accuracy, reduce effect of influence

Active Publication Date: 2022-06-07
GUILIN UNIV OF ELECTRONIC TECH
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] There are still some problems in the above-mentioned prior art model, such as the gray Verhulst model of the prior art uses some outdated information, which makes the prediction result error larger; the BP neural network algorithm has problems in the selection of the initial weight and threshold of the network, resulting in insufficient accuracy of the prediction result high

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
  • A Dynamic Gray Fairhurst Neural Network Landslide Deformation Prediction Method
  • A Dynamic Gray Fairhurst Neural Network Landslide Deformation Prediction Method
  • A Dynamic Gray Fairhurst Neural Network Landslide Deformation Prediction Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0065] The following in conjunction with the accompanying drawings of a particular embodiment of the present invention will be further described, but is not a limitation of the present invention.

[0066] Figure 1 A dynamic gray Ferhast neural network landslide deformation prediction method is shown, including the following steps:

[0067] (1) Establish the original data of the cumulative displacement of the landslide body, establish a GNSS monitoring network in the landslide area, and transmit the three-dimensional coordinate data back to the server through the GPRS or 3G or 4G network, and store the data in the database;

[0068] (2) Pre-processing of data, reading three-dimensional coordinate data from the database and performing pre-processing operations; The data preprocessing comprises Kalman filter smoothing and the use of 3σ criterion to reject wild and outliers that The Kalman filter cannot filter

[0069] (3) Fit the data through the gray Verhulst model, such as in step...

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 gray Fairhurst neural network landslide deformation prediction method, relates to the technical field of landslide monitoring deformation prediction, and solves the technical problem of providing a method with high prediction accuracy of landslide deformation, comprising the following steps: establishing a landslide Raw data of volume cumulative displacement; data preprocessing; data fitting by gray Verhulst model; calculation of residuals and construction of residual sequences; training of GA‑BP neural network; prediction of residual sequences; calculation of combined model predictions. The invention greatly improves the prediction accuracy of landslide deformation.

Description

Technical field [0001] The present invention relates to the field of landslide monitoring deformation prediction technology, in particular to a dynamic gray Ferhast neural network landslide deformation prediction method. Background [0002] Geological disasters are one of the main problems facing human society today, and the geological disasters caused by nature and artificial in China mainly include earthquakes, slope rock and soil displacement, ground deformation and land degradation. According to the statistics of the Department of Land and Resources, in 2015, a total of 8224 geological disasters occurred nationwide, of which 5616 were landslides, accounting for 68.3%. Landslide disaster has become one of the most important geological disasters in China, and one of the important measures to actively and effectively prevent and control geological disasters such as landslides is to conduct long-term observation of deformation hidden danger points such as landslides, issue alarms...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06N3/086G06Q10/04G06N3/045
Inventor 邓洪高姚鹏远孙希延纪元法王守华符强严素清吴孙勇付文涛赵松克李有明
Owner GUILIN UNIV OF ELECTRONIC TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com