The study on rock thermal fractures at sliding surface of Jiweishan landslide

Hui, Luo; Hu, Wei; Zhang, Xianghui; McSaveney, Mauri; Yan, Li

doi:10.1016/j.enggeo.2022.106588

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…Therefore, by combining the LightGBM and SHAP algorithms, this paper proposed an interpretable machine The Jiweishan landslide, a giant sliding rock collapse, occurred on 5 June 2009 in Wulong County. A large number of sliding bodies were cut out from the sliding source area and continued to disintegrate during the movement, eventually forming a body with an average thickness of more than 30 m, a length of 2150 m, and a volume of 700 × 10 4 m 3 , resulting in 74 deaths and 8 injuries [61,62]. Jiwei Mountain in Wulong, composed of hard and soft rock strata, has an inclined thick limestone mountain structure with a steep upper part and a wide and gentle lower part.…”

Section: Lightgbm-shap Hybrid Modelmentioning

confidence: 99%

Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

Sun

Chen

Zhang

et al. 2023

Land

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(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research.

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Section: Lightgbm-shap Hybrid Modelmentioning

confidence: 99%

Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

Sun

Chen

Zhang

et al. 2023

Land

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“…The total volume of the accumulation body caused by the rock avalanche was about 7 million m 3 , covering an area of 0.47 km 2 [40]. The Jiweishan rock avalanche caused 74 deaths and many people were injured, 12 houses were buried and hundreds of meters of rural roads were destroyed, and it destroyed the forest ecosystem in the region [10].…”

Section: Study Areamentioning

confidence: 99%

“…Rockfall can cause serious damage to local forestry resources, farmland, and infrastructure and even cause a loss of life [6][7][8][9]. For example, in June 2009, rockfalls from the Jiweishan rock avalanche in Southwestern China caused extensive damage to trees and villages, resulting in the death and disappearance of 74 people [10][11][12][13]. In addition, in the village of Schneizlreuth in the Alps of Bavaria, Germany, rockfalls have occurred many times in recent years, posing a massive threat to roads and buildings in the region [14].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Mechanism Analysis of Forest Protection against Rockfall in a Large Rock Avalanche Area

Song,

Yang,

Liang

et al. 2023

Forests

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Trees in forests can obstruct falling rocks and serve as a natural barrier to reduce the velocity of falling rocks. Recently, there has been growing interest in utilizing forests to safeguard against potential rockfall. Nevertheless, there is a dearth of research regarding the impact of rock size and forest structure on forest preservation against rockfall. This study takes the Jiweishan rock avalanche that occurred in China in June 2009 as an example to discuss the protection mechanism of forests against rockfall in rock avalanche disasters. Three sizes of rockfalls from the Jiweishan rock avalanche were simulated and analyzed with and without forests using Rockyfor3D software. The findings indicate that forests can mitigate the energy impact of falling rocks. Especially in the debris flow area of rock avalanches, the protective effect of trees on small-sized falling rocks is most obvious, reducing the runout distance and damage range of the debris flow. Moreover, the protective effect of forest structures on rockfall risk was explored. It was found that broad-leaved forests had the best protection against falling rocks, followed by coniferous broad-leaved mixed forests, and coniferous forests had the worst protective effect. Furthermore, increasing forest planting density and tree diameter at breast height (DBH) can result in better protection against rockfall. Thus, rational planning of forest species and planting density in areas of rockfall can effectively reduce the threat of rockfall risk. The research ideas in this study can provide a basis for evaluating the mitigation of rockfall hazards by forests and provide a reference for constructing and planning protective forests in rockfall and rock avalanche hazard areas.

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Assessing the Efficiency of Thermochemical Pressurization in the Jiweishan Rockslide

Deng,

Fan,

Scaringi

et al. 2023

Rock Mech Rock Eng

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The study on rock thermal fractures at sliding surface of Jiweishan landslide

Cited by 5 publications

References 46 publications

Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

Assessment and Mechanism Analysis of Forest Protection against Rockfall in a Large Rock Avalanche Area

Assessing the Efficiency of Thermochemical Pressurization in the Jiweishan Rockslide

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