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

Landslide Disaster Prediction Method Based on Improved Bayesian Network

A Bayesian network and prediction method technology, applied in the field of landslide disaster prediction based on the improved Bayesian network, can solve the problems of low error tolerance rate and low accuracy of the algorithm, and achieve the goal of improving the network fault tolerance rate and forecasting accuracy Effect

Active Publication Date: 2021-11-19
XI'AN POLYTECHNIC UNIVERSITY
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a landslide disaster prediction method based on the improved Bayesian network to solve the problems of low error tolerance and low accuracy of the algorithm used in the existing disaster prediction

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
  • Landslide Disaster Prediction Method Based on Improved Bayesian Network
  • Landslide Disaster Prediction Method Based on Improved Bayesian Network
  • Landslide Disaster Prediction Method Based on Improved Bayesian Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0069] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0070] The landslide disaster prediction method based on the improved Bayesian network of the present invention, such as figure 1 As shown, the specific operation steps are as follows:

[0071] Step 1. Establish a landslide monitoring and early warning system, obtain initial landslide disaster impact factor data, and use principal component analysis (PCA) to extract and screen out the main impact factors after standardized processing;

[0072] Step 2. Divide the sample data of the selected main influencing factors into a training sample set and a test sample set in proportion, and divide the occurrence level of landslide hazards;

[0073] Step 3. with the landslide disaster occurrence grade that step 2 obtains, set up the landslide prediction model based on improved Bayesian, calculate the landslide occurrence probability after introducing f...

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 landslide disaster prediction method based on the improved Bayesian network disclosed by the present invention first establishes a landslide body monitoring and early warning system, obtains the initial landslide disaster impact factor data, and uses the PCA algorithm to extract and screen out the main impact factors after standardized processing; the screened out The main influencing factors of the sample data are divided into training sample set and test sample set in proportion, and the landslide occurrence level is divided; then the landslide prediction model based on the improved Bayesian is constructed, and the landslide occurrence probability after introducing the feature quantity is calculated by weighting , and add a closed-loop learning process to complete the prediction of landslide disasters; the method disclosed in the present invention screens the main influencing factors of landslide disasters, weights the feature quantities based on the improved Bayesian model, and adds a closed-loop learning link to output landslide occurrence probability, increasing fault tolerance rate to improve forecast accuracy.

Description

technical field [0001] The invention belongs to the technical field of geological disaster prediction methods, and relates to a landslide disaster prediction method based on an improved Bayesian network. Background technique [0002] Landslide is an important type of geological disaster, which has a great impact on regional transportation, power stations, factories and mines, urban and rural construction, etc., and seriously threatens people's lives and property safety. The occurrence of landslides is affected by multiple factors, and how to predict the possibility of landslides in advance through the influencing factors has become the focus of attention. [0003] In view of the characteristics of high frequency of landslide disasters and wide range of influence, domestic scholars have used different methods to predict landslide disasters in recent years, but there are still certain limitations. For example, the neural network model has good self-learning and fault-tolerant...

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): G06Q10/04G06K9/62
CPCG06Q10/04G06F18/24155
Inventor 温宗周程少康李丽敏刘德阳李璐
Owner XI'AN POLYTECHNIC UNIVERSITY
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