Novel landslide risk evaluation method

Abstract

The invention discloses a novel landslide risk evaluation method, and the method comprises the following steps: S1, carrying out the statistics of effective rainfall, extracting impervious surface data, and building a water seepage capability model according to the quantitative relation among the rainfall, the impervious surface data and an infiltration relation; S2, taking the gradient, the slopedirection, the water seepage capability index, the vegetation index and the road influence as parameters, and selecting related data of a plurality of points where landslide occurs and does not occurto train the model; and S3, carrying out model construction by using a Logistic regression function to obtain landslide risk divisions, and dividing the landslide risk levels into five risk levels, that is, an extremely low risk level, a low risk level, a medium risk level, a high risk level and an extremely high risk level. According to the method, the rainfall parameters are introduced into theinfiltration relation model, the corresponding landslide risk regionalization map is displayed in real time according to different rainfall, and a certain guiding effect is provided for landslide prevention. Compared with a traditional slope model and a change model for monitoring vertical displacement and horizontal displacement of a region, the landslide risk evaluation model is more suitable for large-scale real-time monitoring.

Description

technical field [0001] The invention belongs to the technical field of landslide early warning, and in particular relates to a novel landslide risk assessment method. Background technique [0002] At present, there are many research reports on the relationship between rainfall and landslide early warning at home and abroad, such as statistical research on the relationship between rainfall and landslides, and analysis on the critical rainfall that causes landslides. For landslide soil, the loess soil structure is loose and the water seepage capacity is strong, and the loess landslide induced by rainfall is more sudden and destructive. Therefore, the early warning of loess landslide has become the focus and hot spot of current research. The deformation model of loess landslides; Niu Quanfu et al. studied the selection criteria of topographic factors of loess landslides; Zhang Shan et al. studied the spatial distribution characteristics of rainfall and loess landslides. [000...

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Problems solved by technology

[0004] However, the currently used quantitative research methods for the relationship between landslides and their disaster-causing factors mainly use slope, aspect, water seepage capacity index, vegetation index, and road layout as model calculation factors. Among them, the water seepage capacity model mostly uses slope stability Factors are the main content. Only geological factors are considered alone, and the influence of rainfall on water seepage capacity is not considered. A more accurate distribution map of landslide water seepage capacity cannot be obtained, and it is not suitable for real-time monitoring of large-scale landslide risks.

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