Discussions on the transformation conditions of Wangcang landslide-induced debris flow

Guo, Jian; Wang, Jiao; Li, Yao; Yi, Shujian

doi:10.1007/s10346-021-01650-4

Cited by 32 publications

(8 citation statements)

References 37 publications

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“…Modified Holmgren and SFLM parameters are used to estimate runout distances in the propagation stage in Flow-R. Considering the difficulty in distinguishing and the connectedness of shallow landslides and debris flow, the decision of whether a movement is a shallow landslide or debris flow depends on a researcher's judgment [64]. It is also impossible to estimate which types of landslides will occur (whether they are debris flows or shallow landslides) in Eocene flysch facies.…”

Section: Modified Holmgrenmentioning

confidence: 99%

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Komu,

Nefeslioglu,

Gokceoglu

2024

IJGI

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Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers.

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Section: Modified Holmgrenmentioning

confidence: 99%

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Komu,

Nefeslioglu,

Gokceoglu

2024

IJGI

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“…Landslides directly or indirectly promote the occurrence of subsequent debris flows by changing the material supply conditions [37]. The transformation methods include dynamic transformation (direct transformation into debris flow during landslide movement) and static transformation (the evolution of landslide deposits is restarted again to form a debris flow) [38,39]. After investigation, was determined that the study area belongs to the static transformation mode.…”

Section: Landslide Interpretationmentioning

confidence: 99%

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

Zhan,

Hu,

Jing

et al. 2024

Sustainability

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The spatial distribution of vegetation in a basin has a far-reaching influence on the potential for sediment separation and transport capacity. However, many landslides induced by strong earthquakes have greatly changed the existing pattern, which further increases the probability of debris flow in a basin during heavy rainfall and has a significant impact on the stability of the basin. Thus, this study selected the debris flow basin in the Qipan catchment of the Wenchuan earthquake area as the research object. Multisource and high-precision remote sensing images were used to analyze the land use changes in the basin, and the index of connectivity (IC) was introduced to analyze the evolution of sediment transport capacity. An ecosystem stability assessment method suitable for post-earthquake debris flow basins was proposed. Through quantitative assessment of the ecosystem stability of the basin after the Wenchuan earthquake in 2008 and the two debris flow events after the earthquake, the dynamic relationship between the debris-flow-prone area and the ecosystem stability of the basin was revealed. The results showed that the stability of the ecosystem in the Qipan catchment increased annually, indicating a stable and substable state. The spatial distribution characteristics were lower in the north and south and greater in the middle. By comparing the evaluation results with the actual terrain change trend, the accuracy and feasibility of the evaluation method are verified. The results of this study provide a scientific basis for the formulation of regional disaster prevention strategies and help to accelerate the improvement of regional stability in debris-flow-prone areas.

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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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Discussions on the transformation conditions of Wangcang landslide-induced debris flow

Cited by 32 publications

References 37 publications

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

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

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