An integrative approach of the physical-based stability index mapping with the maximum entropy stochastic model for risk analysis of mass movements

Memarian, Hadi; Bastami, Shiva Abdi; Akbari, Morteza; Tajbakhsh, Seyed Mohammad; Azamirad, Mahmoud

doi:10.1007/s10668-022-02165-1

Cited by 3 publications

(1 citation statement)

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“…However, physical models cannot automatically calculate thresholds from longterm monitoring data, so they are incompetent in automatic warning of creeping and accelerating stages. The typical physical-mechanical models mainly consist of limit equilibrium (Gowhar et al, 2023;Tesfaye et al, 2023), fine element (Otieno & Shukla, 2024;Wijesinghe et al, 2022), strength reduction (Meko et al, 2023;Rafiei et al, 2023), distinct element (Harnett et al, 2023;Jensen & Moore, 2023), stability index mapping (Memarian et al, 2023), and combination of slope and hydrological models (Chansorn et al, 2023;Mauri et al, 2022;Petpongpan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models

Wang,

Diao,

Guo

et al. 2024

Earth and Space Science

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Automatic deformation forecast and warning of a catastrophic landslide can effectively avoid significant casualties and economic losses. However, currently it has not come to a comprehensive forecast framework covering all the deformation stages of a landslide. Moreover, landslide deformation prediction possesses high error and false alarm rates. This work suggests a novel integrated framework of landslide deformation forecast and warning by coupling machine learning and physical models. The framework can relatively accurately predict all the deformation stages from creeping deformation to critical sliding and features 4 advantages. (a) The forecast indices are established by combining deformation and disaster‐triggering characteristics to improve the prediction accuracy. (b) It leverages the advantage of C5.0 decision tree algorithm in knowledge interpretability to automatically extract deformation forecast criteria. (c) It capitalizes on the precision superiority of a graph convolutional network in time‐series data learning to predict the four deformation stages from creeping deformation to rapidly accelerated deformation. (d) It utilizes the physical and mechanical bases of Morgenstern‐Price method to forecast the critical sliding stage. Zhujiatang Landslide is a large‐scale deep‐seated soil landslide with significant deformation. It is selected as a case study because it has endangered 1,131 persons and may cause a direct financial loss of 100 million RMB. The validating and predicting Accuracy values attain 97.39% and 95.72%, respectively, and the Kappa values reach 0.91 and 0.93, respectively. The landslide will run out when it suffers from a rainstorm with cumulative rainfall of 79.57 mm and an earthquake with a horizontal coefficient of 0.04.

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