Fuzzy Logic Regional Landslide Susceptibility Multi-Field Information Map Representation Analysis Method Constrained by Spatial Characteristics of Mining Factors in Mining Areas

Landslide susceptibility analysis has become a necessary means of pre-disaster portal positioning and scientific early warning. How can an effective zoning model of landslide susceptibility be established to examine the important factors affecting landslide development in coal mine areas? Focusing on the need for a reliability analysis of landslide susceptibility in coal mine areas, landslide cataloging and environmental factor data were used as objects, combined with the knowledge of landslide mechanisms, disaster environmental factors and the spatial correlation of landslide disasters, the frequent landslide area of Jiumine in the main part of Xishan Coalfield was selected as the research area, and more than 50 influencing factors were collected and calculated. Eighteen factors with correlation coefficients of less than 0.3 were selected, and a landslide susceptibility analysis method combining the spatial characteristics of landslide factors and the heuristic fuzzy logic model was proposed. The influence of the fuzzy logic model on the accuracy of landslide susceptibility analysis results under different constraint modes was tested. The model is a mixture of knowledge-driven and data-driven models, and is compared with information model and SVM. Experimental results show that the proposed method is feasible and reliable, and improves the accuracy of model results.

Automated summary

This research selected the Jiumine landslide area as the research area in Xishan Coalfield, collected and calculated more than 50 influencing factors, selected 18 factors with correlation coefficients less than 0.3, and proposed a landslide susceptibility analysis method combining the spatial characteristics of landslide factors and the heuristic fuzzy logic model. Experimental results show that the proposed method is feasible and reliable, and improves the accuracy of model results.