The formation mechanism of landslides in typical fault zones and protective countermeasures: A case study of the Nanpeng River fault zone

Li, Youjun; Hongri, Zhang; Huang, Liming; Li, Hongming; Xuexiao, Wu

doi:10.3389/feart.2022.1092662

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The distance from the fault is another important factor in landslide susceptibility. Faults are fractures in the Earth's crust where significant displacement has occurred [40]. Areas closer to fault lines are generally more prone to landslides due to the increased seismic activity and ground shaking.…”

Section: Figure 5 Types Of Lithologies In the Study Areamentioning

confidence: 99%

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Benmakhlouf,

El Kharim,

Galindo-Zaldivar

et al. 2023

Civ Eng J

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Landslides are a major natural hazard in the mountainous Rif region of Northern Morocco. This study aims to create and compare landslide susceptibility maps in the Western External Rif Chain context using three advanced machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). The landslide database, created by satellite imagery and field research, contains an inventory of 3528 cases of slope movements. A database of 12 conditioning factors was prepared, including elevation, slope, aspect, curvature, lithology, rainfall, topographic wetness index (TWI), stream power index (SPI), distance to streams, distance to faults, distance to roads, and land cover. The database was randomly divided into training and validation sets at a ratio of 70/30. The predictive capabilities of the models were evaluated using overall accuracy (Acc), area under the receiver operating characteristic curve (AUC), kappa index, and F score measures. The results indicated that RF was the most suitable model for this study area, demonstrating the highest predictive capability (AUC= 0.86) compared to the other models. This aligns with previous landslide studies, which found that ensemble methods like RF and XGBoost offer superior accuracy. The most important causal factors of landslides in the study area were identified as slope, rainfall, and elevation, while the influence rate of TWI and SPI was the minimum. By analyzing a larger inventory of landslides on a more extensive scale, this study aims to improve the accuracy and reliability of landslide predictions in a west Mediterranean morphoclimatic context that encompasses a wide variety of lithologies. The resulting maps can serve as a crucial resource for land use planning and disaster management planning. Doi: 10.28991/CEJ-2023-09-12-018 Full Text: PDF

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Section: Figure 5 Types Of Lithologies In the Study Areamentioning

confidence: 99%

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Benmakhlouf,

El Kharim,

Galindo-Zaldivar

et al. 2023

Civ Eng J

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Formation processes and mechanisms of a fault-controlled colluvial landslide in the Qinling-Daba Mountains, China

Zheng,

Cao,

Fan

et al. 2024

Sci Rep

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Faults play a crucial role in shaping the formation and damage patterns of landslides in the mountainous region, particularly in Qinling-Daba (Qinba) area in China. On 6 October 2022, following a 4-day rainfall event totaling 221.5 mm, a landslide occurred in Hanwang Town, Shaanxi Province. The left boundary of the landslide coincided with a fault, which influence the formation and movement development of the landslide. To further understand and quantified the formation process and damage mechanism of the landslide, a comprehensive study was conducted, incorporating field investigations, local rainfall data, and various methods including unmanned aerial vehicles (UAVs), numerical simulations, and laboratory test. The results indicate that fault dictate the formation of the Lijiaping landslide by influencing the mechanical strength of the rock mass and the catchment topography in the landslide area. Due to fault, the rock mass in the landslide area is high fragmentation, with a softening coefficient of about 0.52. Weathering resulted in numerous residual and slope sediments in the landslide area, providing ample material for the landslide. Meanwhile, the fault activity led to a wedge-shaped topography in the landslide area, with an average Terrain Wetness Index (TWI) of 3.43, significantly higher than the Hanwang Township average of 1.47. This creates a hydrogeological structure favorable for landslides. Numerical simulations revealed that the maximum velocity of the landslide reached 5.05 m/s and the maximum displacement was 53.18 m, both occurring in the central part of the landslide. These findings offer crucial scientific insights for understanding and preventing similar geological hazards.

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The formation mechanism of landslides in typical fault zones and protective countermeasures: A case study of the Nanpeng River fault zone

Cited by 2 publications

References 19 publications

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Formation processes and mechanisms of a fault-controlled colluvial landslide in the Qinling-Daba Mountains, China

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