Characteristics and emergency mitigation of the 2018 Laochang landslide in Tianquan County, Sichuan Province, China

Chen, Zhuo; Song, Danqing; Dong, Lizhi

doi:10.1038/s41598-021-81337-x

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…Selection of appropriate explanatory variables is a critical step in the LS assessment, but no commonly accepted selection criterion is available [40]. Based on landslide behavior across the Hengduan Mountains region [34,51,64,65], 50 explanatory variables were accepted as input variables for LS assessment. For the LS evaluation across the large-scale and spatially heterogeneous region, we subdivided these explanatory variables into three categories, i.e., static, dynamic, and triggering factors [39,41,66].…”

Section: Explanatory Variablesmentioning

confidence: 99%

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

Qiang

Singh

et al. 2022

Remote Sensing

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Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.

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Section: Explanatory Variablesmentioning

confidence: 99%

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

Qiang

Singh

et al. 2022

Remote Sensing

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“…The topography, geology and climate in China are extremely complex, with seven major water systems, five major earthquake zones and twenty-three seismic zones [1]. The coupling effect of earthquake, meteorology, water and other disaster-causing factors intensifies the frequency and scale of landslide disasters, which has a significant impact on the construction and operation safety of major water conservancy and traffic projects [2].…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of a Local Precise Reinforcement Method for Dynamic Stability of Rock Slope under Earthquakes Using Continuum–Discontinuum Element Method

et al. 2023

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The slope reinforcement scheme has an important influence on the prevention and control of landslides. A reasonable reinforcement scheme can improve the reliability, economy and efficiency of landslide resistance. It is urgent to establish a local precise reinforcement method for landslides on the basis of clear process and the instability modes of landslides. Taking a high-steep anti-dip rock slope as an example, six numerical models are established by using the continuum–discontinuum element method (CDEM) to carry out seismic damage and dynamic analysis of slopes. By comparing the seismic response and damage characteristics of being unstrengthened, local precise reinforcement and overall reinforcement models, the applicability of the proposed local precise reinforcement method for the slopes is discussed. The results show that the determination of the dynamic amplifying effect and seismic damage characteristics of slopes is the primary prerequisite of the local precise reinforcement method. The dynamic amplification effect of the slope toe, crest and shallow slope surface are much larger, that is, they are the potential reinforcement areas. The local precision reinforcement times should be controlled within a certain number of times, and the slope after the first three times of the local reinforcement effect is the best. However, more than three times after the reinforcement effect it becomes worse. Moreover, the dynamic amplification effect, the equivalent crack ratio and the mechanical energy of the slope after three times of local precision reinforcement are similar to the overall reinforcement effect, which indicates that local precision reinforcement has good feasibility. This work can provide references for landslide disasters prevention and control.

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“…Landslide characterisation is described through various means, e.g. landslide mechanisms and occurrence force (Leroueil 2001;Take et al 2004;Huang et al 2022), failure mode analysis/simulation (Liu et al 2015;Sidle and Bogaard 2016;Han et al 2019a), sensitivity/reliability analysis (Rathod and Rao 2012;Nguyen and Likitlersuang 2019;Huang et al 2020), and the design of effective countermeasure and their field optimisation (Popescu and Sasahara 2009;Han et al 2019b;Chen et al 2021). In addition to the physical science investigation, socio-economic analysis of the landslide issues helps to infer the vulnerability of the people living on dangerous slopes and effectively promotes the disaster risk governance (Antronico et al 2020;Vij et al 2020;Alam and Ray-Bennett 2021;Huang et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessing the effectiveness of landslide slope stability by analysing structural mitigation measures and community risk perception

et al. 2023

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Rainfall-induced landslides seriously threaten hilly environments, leading local authorities to implement various mitigation measures to decrease disaster risk. However, there is a significant gap in the current literature regarding evaluating their effectiveness and the associated community risk perception. To address this gap, we used an interdisciplinary and innovative approach to analyse the slope stability of landslides, evaluate the effectiveness of existing structural mitigation measures, and assess the risk perception of those living in danger zones. Our case study focused on the Kutupalong Rohingya Camp (KRC) in Cox’s Bazar, Bangladesh, which is home to over one million Rohingya refugees from Myanmar. Although various structural and non-structural countermeasures were implemented in the KRC to mitigate the impact of landslides, many of them failed to prevent landslides from occurring. We utilised a variety of methods from the physical sciences, including the infinite slope, limit equilibrium (LEM), and finite element (FEM) approaches, to calculate the factor of safety (FoS) for specific slopes. Additionally, in the social sciences domain, we conducted a questionnaire survey of approximately 400 Rohingya participants to assess the community’s perception of the interventions and the degree of disaster risk. Our findings indicated that slopes with a gradient greater than 40° were unstable (FoS < 1), which was present throughout the entire KRC area. The effectiveness of the LEM and FEM methods was evaluated for four dominant slope angles (40°, 45°, 50°, and 55°) under varying loads (0, 50, and 100 kN/m2). The slopes were found to be stable for lower slope angles but unstable for higher slope angles (> 50°) and increased overburden loads (50–100 kN/m2). Different mitigation measures were tested on the identified unstable slopes to assess their effectiveness, but the results showed that the countermeasures only provided marginal protection against landslides. Survey results revealed that at least 70% of respondents believed that concrete retaining walls are more effective in reducing landslide occurrence compared to other measures. Additionally, about 60% of the respondents questioned the reliability of the existing structural mitigation measures. The study also found that the cohesion and friction angle of lower sandstone and the cohesion of upper soil layers are important factors to consider when designing and implementing slope protection countermeasures in the KRC area.

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Characteristics and emergency mitigation of the 2018 Laochang landslide in Tianquan County, Sichuan Province, China

Cited by 10 publications

References 31 publications

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

Numerical Investigation of a Local Precise Reinforcement Method for Dynamic Stability of Rock Slope under Earthquakes Using Continuum–Discontinuum Element Method

Assessing the effectiveness of landslide slope stability by analysing structural mitigation measures and community risk perception

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