Novel landslide risk evaluation method

A landslide risk and evaluation method technology, applied in the field of landslide early warning, can solve the problems of not considering the influence of rainfall seepage capacity, not being able to obtain a relatively accurate distribution map of landslide seepage capacity, and not applicable to real-time monitoring of landslide risk

Active Publication Date: 2019-11-22
LANZHOU JIAOTONG UNIV
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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 sta

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Embodiment 1

[0048] 1. Algorithm principle

[0049] 1.1. Construction of water seepage capacity model

[0050]Water seepage capacity is a physical quantity describing how much rainwater seeps into different geological types during rainfall, and is the basis and important component of landslide risk assessment. Traditional water seepage capacity models mostly focus on slope stability as the main content of discussion. The present invention combines rainfall Constructing a new type of seepage capacity model is of great significance for large-scale landslide risk assessment.

[0051] 1.1.1 Infiltration model with layered assumptions

[0052] Layered hypothetical infiltration model diagram reference figure 1 , the model is as follows:

[0053] Saturation layer: θ (z) = θ s , 0≤z≤L 1 ; (4)

[0054] transition layer:

[0055] Dry soil layer: θ (z) = θ H , z≥L 2 ; (6)

[0056] In the formula, θ (z) represents the infiltration rate, θ s Indicates the soil moisture content after rai...

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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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Application Information

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IPC IPC(8): G06F17/50G06Q50/26
CPCG06Q50/26Y02A90/10
Inventor 杨树文王兆华闫如柳张珊朱晓霞贾鑫申顺发
Owner LANZHOU JIAOTONG UNIV
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