The effect of topography on landslide kinematics: a case study of the Jichang town landslide in Guizhou, China

Guo, Jian; Yi, Shujian; Yin, Yi; Cui, Yifei; Qin, Mingyue; Li, Tonglu; Wang, Chenyang

doi:10.1007/s10346-019-01339-9

Cited by 59 publications

(21 citation statements)

References 39 publications

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“…A large earthquake (magnitude > 6) can trigger landslides of shallow soil, as well as large, deep rock landslides [51]. A landslide's initiation is controlled by different failure mechanisms associated with material types, topography, and geotechnical features [52][53][54]. Landslides in bedrock are especially strongly influenced by their structure and preaccumulated failure, which consequently result in complex failure mechanisms compared with soil landslides [55].…”

Section: Performance Of Rock Landslide Predictionmentioning

confidence: 99%

Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

Cui

et al. 2021

Remote Sensing

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An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.

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Section: Performance Of Rock Landslide Predictionmentioning

confidence: 99%

Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

Cui

et al. 2021

Remote Sensing

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“…e upper part of the Shuicheng landslide was steep with an average dip angle of 28°, while the lower part was slightly gentler with an average dip angle of 10°. ere were multiple gentle slopes and terraces in the trailing edge and central area of the landslide, which conducted to the collection of rainwater [38]. In addition, the narrow gullies on both sides of the landslide formed a favorable circulation channel for the sliding body.…”

Section: Topography and Landformmentioning

confidence: 99%

“…e study area is located in the middle of the Weining northwest trending structure deformation zone in the Liupanshui fault depression of the Yangtze platform [38]. e exposed strata mainly include the Carboniferous (C), Permian (P), and Triassic (T).…”

Section: Geological Conditionsmentioning

confidence: 99%

Dynamic Analysis of the High‐Speed and Long‐Runout Landslide Movement Process Based on the Discrete Element Method: A Case Study of the Shuicheng Landslide in Guizhou, China

Xia

Liu

et al. 2021

Advances in Civil Engineering

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On July 23, 2019, a high-speed and long-runout landslide occurred in Jichang Town, Shuicheng County, Guizhou Province, China, causing 42 deaths and 9 missing. This paper used the discrete element software MatDEM to construct a three-dimensional discrete element model based on digital elevation data and then simulated and analyzed the movement and accumulation process of the landslide. The maximum average velocity of the source area elements reached 14 m/s when passed through the scraping area; meanwhile, the velocity of the scraping area elements increased rapidly. At 90 s, the maximum displacement of the source area elements reached 1358.5 m. The heat generated during the movement of the landslide was mainly the frictional heat, and the frictional heat increased sharply when the source area elements passed through the scraping area. The change of frictional heat has a certain positive correlation with the velocity of the scraping area elements. Finally, the volume of the scraping area elements was 2.4 times greater than the source area elements in the deposits. The scraping effect increases the volume of the sliding body and expands the impact area of the landslide disaster. Additionally, by setting different compressive and tensile strengths as well as internal friction coefficients to analyze the influences of their value changes on the landslide movement process, the results show that the smaller the strengths and internal friction coefficient of the model, the greater the depth and area of the scraping area, which will result in a thicker accumulation; meanwhile, the average displacement, average velocity, and heat will also increase.

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“…In addition, soil movement can also be caused by human activities, such as water extraction, mining activities, cutting slopes for road construction, and others (Bateson et al, 2015;Hearn et al, 2020;Persichillo et al, 2017). In addition, ground movements due to landslides can cause topographic changes (Dehghani & Javadi, 2013;Guo et al, 2020). Therefore, monitoring and mapping ground movements such as landslides are necessary to minimize the risk of landslides and other ground movements (Crosetto et al, 2018;Skrzypczak et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Landslide Surface Deformation Analysis Using Sbas-Insar in the Southern Part of the Sukabumi Area, Indonesia

Rosyidy

Dimyati²,

Shidiq³

et al. 2021

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Landslide is a natural phenomenon that frequently occurs on the Java Island of Indonesia, causing significant damage and casualties. Due to advances in remote sensing technology, radar imaging can detect and evaluate ground surface deformation. This study examines the ground surface deformation and displacement in each landslide location in terms of spatial and temporal and identifies the different types and characteristics of landslides in the Sukabumi area of West Java, Indonesia. The Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) methodology was used in this study, and the DinSAR method was applied. We combined the LiCSAR data product with the Python coding-based LiCSBAS processing package to derive a surface displacement value at each landslide location. The results show that the DinSAR approach can detect surface deformation by integrating the LiCSAR product with the Python coding-based LiCSBAS processing software. According to quantitative data, the research area experienced surface deformation with a surface displacement velocity of -36,297 mm/year to 58,837 mm/year. The ground surface displacement at each landslide location ranged from -9.79 mm/year to +33.69 mm/year, with most of the landslides occurring on moderate to steep slopes (16 0 -35 0 ). These results are suitable to use for support regional development planning in reducing losses and casualties.

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The effect of topography on landslide kinematics: a case study of the Jichang town landslide in Guizhou, China

Cited by 59 publications

References 39 publications

Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

Dynamic Analysis of the High‐Speed and Long‐Runout Landslide Movement Process Based on the Discrete Element Method: A Case Study of the Shuicheng Landslide in Guizhou, China

Landslide Surface Deformation Analysis Using Sbas-Insar in the Southern Part of the Sukabumi Area, Indonesia

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