Probabilistic Model of Landslide Processes Based on Markov Chains

Victorov, A. S.

doi:10.5593/sgem2015/b12/s2.077

Cited by 1 publication

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“…Generally, most scholars have used different types of functions for fitting, such as the Verhulst logistic function and Verhulst inverse function [37], which can be used to predict the displacement of landslides with time and divide the deformation stages. Meanwhile, random methods are the second most widely used type, such as the GM model [38][39][40][41][42][43], regression model [44], the BP network model [5,45,46], and the Markov Chain model [47][48][49]. These methods are applied to predict landslide displacement in a short time.…”

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confidence: 99%

Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

Bao

Zhang

et al. 2021

Energies

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The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day2, is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

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