Monitoring tilting angle of the slope surface to predict loess fall landslide: an on-site evidence from Heifangtai loess fall landslide in Gansu Province, China

Wang, Honghui; Zhong, Pan; Xiu, Dehao; Zhong, Yunshun; Peng, Dalei; Xu, Qiang

doi:10.1007/s10346-021-01727-0

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…However, Savvaidis (2003) noted that techniques vary from case to case, depending on the expected risk, accessibility of the area, the potential for damage, and the availability of resources. This was also confirmed by many other authors who studied landslide monitoring techniques over the last 20 years (Tofani et al 2013;Zhang et al 2013;Notti et al 2015;Martha et al 2017;Peternel et al 2017;Piciullo et al 2018;Pecoraro et al 2019;Raspini et al 2019;Šegina et al 2020;Bonì et al 2020;Wang et al 2022). Landslide monitoring techniques can provide data at different temporal resolutions and at different phases of the event (before, during, or after).…”

Section: Introductionsupporting

confidence: 64%

Landslide monitoring techniques in the Geological Surveys of Europe

et al. 2023

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Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020).

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Section: Introductionsupporting

confidence: 64%

Landslide monitoring techniques in the Geological Surveys of Europe

et al. 2023

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“…The loess in the study area is atypical, and the loess seismic landslides of the study area are different from those in the Loess Plateau. For typical loess landslides, the monitoring and warning of loess landslides geological hazards (Wang et al, 2022) and the establishment of a landslide distribution database in the Loess Plateau (Xu et al, 2020c) have been mainly carried out, including the loess landslide hazard assessment (Zhuang et al, 2016), spatial distribution prediction of loess landslides (Huang et al, 2021), drawing landslide hazard distribution maps (Peng et al, 2019), and so on based on GIS (Li et al, 2021). The research results show that the loess landslides in the Loess Plateau are characterized by large scale, large quantity, gentle slope angle, and long sliding distance (Yan et al, 2021); particularly, the influence of soil is the key for loess landslides to have these characteristics.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of loess landslides triggered by the 1927 Mw8.0 earthquake that occurred in Gulang County, Gansu Province, China

Zhong

Chen

et al. 2022

Front. Environ. Sci.

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The 1927 Gulang Mw8.0 earthquake was one of the largest earthquakes in Gansu, China, which triggered lots of loess landslides. However, the loess of the study area is atypical and different from that of the Loess Plateau. Meanwhile, there are few systematic research studies on the characteristics of these seismic landslides. Combined with previous studies and field investigations, the landslide distribution has been revealed through the visual interpretation of remote sensing images of the study area. The relationships between landslides and various influencing factors have been explored through spatial analysis of geographic information science (GIS). Furthermore, comparisons have been made between the Gulang earthquake landslides and other seismic landslides in the Loess Plateau from macroscopic and microcosmic perspectives. A total of 807 earthquake landslides have been interpreted, with a total landslide area of 256.14 km2. The region with the seismic intensity of Ⅹ is the dominant area of landslide distribution. Also, most landslides are distributed at slope angles of (10° and 40°). The relative elevation difference of (100 m and 300 m) is the high-incidence range of landslides. Compared with seismic landslides in the Loess Plateau, the slope angle and the relative elevation difference of most landslides in the study area are larger. The strength of the loess structure in the study area is stronger than that in the Loess Plateau from the view of physical properties and microstructure.

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“…In geotechnical engineering, slope stability analysis has been a significant research area. In China, nearly 800 people died or missed every year caused by slope instability, resulting in economic losses of more than 600 million dollars (Wang et al, 2022). Therefore, it is essential to perform slope stability analysis to ensure the reliability of slopes and the safety of people in the vicinity.…”

Section: Introductionmentioning

confidence: 99%

Slope stability prediction based on adaptive CE factor quantum behaved particle swarm optimization-least-square support vector machine

Yang

2023

Front. Earth Sci.

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Since the prediction of slope stability is affected by the combination of geological and engineering factors with uncertainties such as randomness, vagueness and variability, the traditional qualitative and quantitative analysis cannot match the recent requirements to judge them accurately. In this study, we expect that the adaptive CE factor quantum behaved particle swarm optimization (ACE-QPSO) and least-square support vector machine (LSSVM) can improve the prediction accuracy of slope stability. To ensure the global search capability of the algorithm, we introduced three classical benchmark functions to test the performance of ACE-QPSO, quantum behaved particle swarm optimization (QPSO), and the adaptive dynamic inertia weight particle swarm optimization (IPSO). The results show that the ACE-QPSO algorithm has a better global search capability. In order to evaluate the stability of the slope, we followed the actual project and research literature and selected the unit weight, slope angle, height, internal cohesion, internal friction angle and pore water pressure as the main indicators. To determine whether the algorithm is scientifically and practically feasible for slope deformation prediction, the ACE-QPSO-, QPSO-, IPSO-LSSVM and single least-square support vector machine algorithms were trained and tested based on a real case of slope project with six index factors as the input layer of the LSSVM model and the safety factor as the output layer of the model. The results show that the ACE-QPSO-LSSVM algorithm has a better model fit (R2=0.8030), minor prediction error (mean absolute error=0.0825, mean square error=0.0110) and faster convergence (second iteration), which support that the ACE-QPSO-LSSVM algorithm emthod is more feasible and efficient in predicting slope stability.

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Monitoring tilting angle of the slope surface to predict loess fall landslide: an on-site evidence from Heifangtai loess fall landslide in Gansu Province, China

Cited by 13 publications

References 16 publications

Landslide monitoring techniques in the Geological Surveys of Europe

Landslide monitoring techniques in the Geological Surveys of Europe

Characteristics of loess landslides triggered by the 1927 Mw8.0 earthquake that occurred in Gulang County, Gansu Province, China

Slope stability prediction based on adaptive CE factor quantum behaved particle swarm optimization-least-square support vector machine

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