Feature function space filter value-based regressive landslide hazard logistic analysis method

A logistic regression and feature function technology, applied in the field of geostatistics and spatial analysis, can solve the problem of low model accuracy, achieve accurate simulation and prediction, and improve the goodness of fit and prediction accuracy.

Active Publication Date: 2018-05-15
WUHAN UNIV
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Problems solved by technology

[0025] In order to solve the problem that the accuracy of the model is not high due to the influence of the spatial autocorrelation between variables when the Logistic regression model is applied to landslide disaster analysis, the present invention provides a Logistic regression analysis method for landslide disasters based on the characteristic function spatial filter value

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  • Feature function space filter value-based regressive landslide hazard logistic analysis method
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  • Feature function space filter value-based regressive landslide hazard logistic analysis method

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[0044] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0045] The core problem to be solved by the present invention is: when using the Logistic regression model to analyze landslide disasters, the influence of spatial autocorrelation between variables on model accuracy and fitting goodness is eliminated by using the feature function space filtering method.

[0046] See attached figure 1 , perform the following steps:

[0047] Step 1: Select and process the landslide sample data (the sum of landslide point samples and non-landslide point samples), including obtaining landslide point samples and their correspondi...

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Abstract

The invention discloses a space filter value-based logistic regressive landslide hazard analysis method. Aiming at analytical investigations of landslide hazards, a space filter value thought is imported into a common logistic regression model so as to design a regressive landslide analysis algorithm which comprises the steps of non-landslide site selection, disaster-inducing factor obtaining andgrading, adjacent matrix construction, feature value and feature vector calculation, stepwise regression feature vector selection and regressive modeling. According to the method, the problem that themodel precision is not high as logistic regression models are influenced by spatial autocorrelation between variables can be solved. By adding filter value operators which are constructed by utilizing selected feature vectors into logistic regression models, autocorrelation influences of residuals can be effectively filtered, the fitting goodness and prediction correctness of the regression models can be improved and correct simulation and prediction for landslide hazards can be realized.

Description

technical field [0001] The invention belongs to the field of geoscience statistics and spatial analysis, in particular to a landslide disaster logistic regression analysis method based on characteristic function spatial filter values. Background technique [0002] Landslide disasters are one of the most common geological disasters. The analysis of landslide disasters mainly includes two categories: qualitative analysis and quantitative analysis (Yalcin et al., 2011, see Background Document 1). The professional knowledge and in-depth understanding and investigation of the research area are used to analyze and evaluate landslide disasters, which are mostly used for small-scale or a specific accident. The main methods used are expert experience method, weighted linear sum method and analytic hierarchy process. Quantitative methods for landslide analysis are mostly based on sound theoretical foundations, and are used to analyze and study landslide disasters from a macro perspect...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/15G06F17/16
CPCG06F17/15G06F17/16
Inventor 陈玉敏李慧芳周江杨家鑫张静祎陈娒杰方涛
Owner WUHAN UNIV
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