Landslide disaster susceptibility spatial prediction method based on clustering-information quantity coupling model

Abstract

The invention provides a landslide disaster susceptibility space prediction method based on a clustering-information quantity coupling model. Clustering grading is carried out on evaluation indexes ofthe terrain evaluation factors by adopting a K-means algorithm, the internal relation of each terrain evaluation factor in a single evaluation unit is fully considered, and grading is carried out onthe distance type evaluation factors from the aspect of numerical value by adopting a natural breakpoint method; the information amount of each evaluation factor under different levels is calculatedaccording to the information amount model; and according to the sum of the information amounts of the grades of the evaluation factors of each evaluation unit, the landslide information amount of theevaluation unit is obtained so as to obtain the landslide information amounts of all the evaluation units in the map, and information amount grading is carried out in combination with a frequency ratio model so as to finally obtain a landslide susceptibility grade division map. The method has high spatial prediction precision of landslide disaster susceptibility, and can be used as a basis for disaster prevention and control and disaster treatment.

Description

technical field [0001] The invention relates to geological disaster assessment and prediction technology. [0002] technical background [0003] Geological hazards are formed under the interaction of multiple evaluation factors. Systematic analysis of the contribution of each evaluation factor to the susceptibility of landslide disasters based on information entropy can accurately reflect the development characteristics of geological hazards. The evaluation factor can be some kind of terrain or landform feature. As an effective regional geological disaster prediction method, the traditional information model converts the measured data of the evaluation factors affecting regional stability into information reflecting regional stability. The greater the amount of information, the more prone to geological disasters. high. [0004] The establishment of the information amount model is to classify each evaluation factor, and then calculate the information amount of each evaluatio...

AI Technical Summary

Problems solved by technology

The applicant found that this grading method will ignore the similarity between non-adjacent categories, and there is no reliable basis for dividing breakpoints. Elements near some intervals will be excluded, which will cause some meaningful intervals to be ignored, and eventually lead to the establishment of The accuracy of the informative model of the

Images