Stability Analysis of Jointed Rock Cutting Slope Based on Discrete Element Method

Zhu, Wei Shen; Gao, Liang; Zhao, Yingai; Yanɡ, Chao; Sun, Wei; Yu, Pengqiang

doi:10.1155/2022/4915820

Cited by 2 publications

(1 citation statement)

References 30 publications

(30 reference statements)

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“…Distance to fault has an association with the degree of fracture and weathering. Thus, has a role on landslide susceptibility (Einstein et al, 1983;Farrokhnia et al, 2011;You et al, 2018;Zhu et al, 2022). The furthest distance to fault lineament on the study area is reaching 20,997 m.…”

Section: Geological Factorsmentioning

confidence: 89%

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

Melati,

Umbara,

Astisiasari

et al. 2024

Environ Earth Sci

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Landslide is one of the most highly frequent natural hazards that can bring serious casualties. One of the most susceptible landslide regions in Indonesia is Bogor area (the Regency and City of Bogor), which records the highest landslide events in the Province of West Java, Indonesia. An assessment of landslide susceptibility is one of the mitigation measures that can spatially model the zone of landslide hazard.Recently, the Landslide Susceptibility Mapping (LSM) model has been developed by using Machine Learning (ML) algorithms. However, there is still no agreement yet on which ML technique is the most appropriate for LSM. Accordingly, this paper aims to explore and compare the 7 ML algorithms for generating the most promising LSM. The LSM uses the available 13 landslide causal factors and a dataset consisting of 822 authorized landslide records and 822 prepared nonlandslide points. The resulting LSMs are classified into 5 susceptibility levels, and evaluated through the Area Under Curve (AUC) of the Receiver Operating Curve (ROC) and statistical indices (sensitivity, specificity, precision, F1-score, and accuracy). The resulting LSMs present that: (1) the very-high (VH) class has the largest area percentage in all LSM models, (2) generally, the 7 MLs perform excellent for achieving >90% AUC value, except for the Decision Tree (DT) (87.84%) in model classification, (3) moreover, the overall accuracy (ACC) reflects that Random Forest (RF) outperforms the other MLs in model prediction. With this promising result, ML-based LSM models can be promoted as one of the mitigation measures for landslide disaster management.

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Section: Geological Factorsmentioning

confidence: 89%

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

Melati,

Umbara,

Astisiasari

et al. 2024

Environ Earth Sci

View full text Add to dashboard Cite

show abstract

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

Melati,

Umbara,

Astisiasari

et al. 2023

Preprint

View full text Add to dashboard Cite

Landslide is one of the most highly frequent natural hazards that can bring serious casualties. One of the most susceptible landslide regions in Indonesia is Bogor area (the Regency and City of Bogor), which records the highest landslide events in the Province of West Java, Indonesia. An assessment of landslide susceptibility is one of the mitigation measures that can spatially model the zone of landslide hazard. Recently, the Landslide Susceptibility Mapping (LSM) model has been developed by using Machine Learning (ML) algorithms. However, there is still no agreement yet on which ML technique is the most appropriate for LSM. Accordingly, this paper aims to explore and compare the 7 ML algorithms for generating the most promising LSM. The LSM uses the available 13 landslide causal factors and a dataset consisting of 822 authorized landslide records and 822 prepared non-landslide points. The resulting LSMs are classified into 5 susceptibility levels, and evaluated through the Area Under Curve (AUC) of the Receiver Operating Curve (ROC) and statistical indices (sensitivity, specificity, precision, F1-score, and accuracy). The resulting LSMs present that: (1) the very-high (VH) class has the largest area percentage in all LSM models, (2) generally, the 7 MLs perform excellent for achieving >90% AUC value, except for the Decision Tree (DT) (87.84%) in model classification, (3) moreover, the overall accuracy (ACC) reflects that Random Forest (RF) outperforms the other MLs in model prediction. With this promising result, ML-based LSM models can be promoted as one of the mitigation measures for landslide disaster management.

show abstract

Stability Analysis of Jointed Rock Cutting Slope Based on Discrete Element Method

Cited by 2 publications

References 30 publications

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

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