A data driven efficient framework for the probabilistic slope stability analysis of Pakhi landslide, Garhwal Himalaya

Falae, Philips Omowumi; Agarwal, Ekansh; Pain, Anindya; Dash, Rajesh Kumar; Kanungo, Debi Prasanna

doi:10.1007/s12040-021-01641-y

Cited by 7 publications

(1 citation statement)

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“…is a nonlinear regression algorithm used for classification and regression problems. MARS models the relationship between multiple inputs and one output using a series of piecewise linear functions called basis functions [106,107]. Taking the linear basis function as an example, other basis functions are similar.…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

Xu,

He,

Shan

et al. 2023

Modelling

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In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given.

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Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%