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Dangerous rock falling rock migration distance prediction method and device based on GPR

A technique for moving distances and falling dangerous rocks. It is applied in complex mathematical operations, special data processing applications, instruments, etc., and can solve problems such as difficulty in considering uncertain factors and difficult calculation results to achieve satisfactory results.

Pending Publication Date: 2019-12-27
GUILIN UNIVERSITY OF TECHNOLOGY
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  • Application Information

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Problems solved by technology

The migration of dangerous rockfall is a complex nonlinear problem with great uncertainty, which makes it difficult for traditional research methods to consider the influence of uncertain factors, making it difficult to achieve satisfactory results in calculation results

Method used

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  • Dangerous rock falling rock migration distance prediction method and device based on GPR
  • Dangerous rock falling rock migration distance prediction method and device based on GPR
  • Dangerous rock falling rock migration distance prediction method and device based on GPR

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Embodiment Construction

[0062] The invention provides a GPR-based prediction method and device for the migration distance of dangerous rockfall, which effectively solves the problem of predicting the non-linear migration distance of dangerous rockfall. It is a prediction method with more accurate prediction results, strong applicability, strong generalization ability, self-adaptive parameters and easy implementation.

[0063] The present invention will be further illustrated below in conjunction with the accompanying drawings and specific test examples. It should be understood that these examples are only used to illustrate the present invention, and should not be construed as limiting the patent.

[0064] The nature of Gaussian process regression is determined by the mean function and covariance function, and its expression is as follows:

[0065] f(x)~GP(m(x),k(x,x')) (1)

[0066] in:

[0067] x,x'∈R d for any random variable

[0068] In actual calculation, the output vector is affected by n...

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Abstract

The invention relates to a dangerous rock falling rock migration distance prediction method and device based on GPR. According to the method and the device, a small number of training samples can be learned, and the optimal hyper-parameter is obtained through logarithm likelihood maximization, so that the prediction result of the dangerous rock falling rock migration distance is obtained. Researchresults show that a Gaussian process regression (GPR) machine learning model for dangerous rock falling rock migration distance prediction is feasible, and the prediction method of the dangerous rockfalling rock migration distance prediction has the advantages of being high in prediction precision, high in adaptability, self-adaptive in parameter and the like.

Description

technical field [0001] The invention relates to a GPR-based prediction method and device for the migration distance of dangerous rockfall, which belongs to the field of dangerous rockfall prediction and machine learning. Background technique [0002] With the construction and development of mountainous areas in our country, the number of roads, bridges and other projects in mountainous areas is increasing day by day. The complex topography and landforms of mountainous areas will not only bring safety hazards during construction, but also cause disasters such as dangerous rocks and falling rocks under the influence of earthquakes, heavy rainfall and other disaster weather in the future, endangering people's safety. [0003] Dangerous rock collapse has become one of the three major geological disasters in mountainous areas of our country, and it is also one of the important restrictive factors for the development and construction of mountainous areas in our country. Rockfalls...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06F17/18G06N20/00
CPCG06F17/18G06N20/00
Inventor 张研吴康丽苏国韶曾建斌曾召田邝贺伟
Owner GUILIN UNIVERSITY OF TECHNOLOGY