Landslide early warning, case studies from Southwest China

Ju, Nengpan; Jian, Huang; He, Chuan; Asch, Th.W.J. van; Huang, Runqiu; Fan, Xiang; Liu, Fangzhou; Xiao, Yang; Wang, Jue

doi:10.1016/j.enggeo.2020.105917

Cited by 52 publications

(19 citation statements)

References 66 publications

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“…Landslides are a common natural disaster caused by external stimuli such as rainfall, earthquakes, and human activities (Andersson-Sköld et al 2013 ; Dai et al 2002 ; Ju et al 2020 ), resulting in an expansion of shear stress or a decline in soil shear resistance, which easily occurs in mountainous areas (Arnone et al 2011 ; Dai et al 2002 ). It has the characteristics of rapidness, wide range and great destruction (Zhang et al 2020 ).…”

Section: Simulation Training Systems Based On Different Phases Of Eme...mentioning

confidence: 99%

Review on visualization technology in simulation training system for major natural disasters

Sun

Cao

et al. 2022

Nat Hazards

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Section: Simulation Training Systems Based On Different Phases Of Eme...mentioning

confidence: 99%

Review on visualization technology in simulation training system for major natural disasters

Sun

Cao

et al. 2022

Nat Hazards

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“…Nearly 90% of these landslides occurred in southern China, causing huge casualties and economic and property losses. Compared with the high-cost landslide treatment, monitoring and giving early warning of landslides with potential sliding risks is more economical and effective [2][3][4][5]. For the early warning, we should obtain the deformation parameters (surface displacement, deep displacement, crack width), changes in rock and soil parameters (water content, earth pressure), and other factors (groundwater level, rainfall, etc.).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Early Warning Method for the Landslide Acceleration Process Based on Automated Monitoring Data

Bai

Zhu

et al. 2022

Applied Sciences

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The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lishanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides.

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“…After landslides, real-time monitoring has always been a key to the on-site emergency rescue. Substantial techniques have been utilized, involving Global Navigation Satellite Systems and the in-place inclinometer (IPI) [23][24][25]. However, these traditional methods are usually time-consuming and are incapable of emergency rescue.…”

Section: Introductionmentioning

confidence: 99%

Application of UAV and GB-SAR in Mechanism Research and Monitoring of Zhonghaicun Landslide in Southwest China

Liu

Mei

et al. 2021

Remote Sensing

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This paper presents a recent rainfall-induced landslide in China that occurred on August 21, 2020 and resulted in nine deaths. The sliding material traveled a distance of 800 m, with an altitude difference of about 180 m. A field investigation, remote sensing based on an unmanned aerial vehicle (UAV), in situ monitoring, and a rainfall data analysis were carried out to reveal the deposit characteristics, causative factors, post-landslide behavior, and the mechanism of the landslide. A saltatory micro-relief of the original slope determined the multiple-stage failure type of the slide, and also promoted the entrainment effect during the landslide movement. After the first-initiation sliding stage, the motion of this landslide involved typical progressive movement, and over time, the style of the runout generally turned into a flow-like form. Furthermore, the antecedent cumulative rainfall of 149.5 mm directly contributed to the occurrence of the landslide. Using the GB-SAR early warning system, the post-landslide residual failure was successfully predicted 10 min in advance. The combination of the UAV and GB-SAR technique can surely be beneficial for other inaccessible landslide investigations as well and improves the emergency rescue security.

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Landslide early warning, case studies from Southwest China

Cited by 52 publications

References 66 publications

Review on visualization technology in simulation training system for major natural disasters

Review on visualization technology in simulation training system for major natural disasters

A Hybrid Early Warning Method for the Landslide Acceleration Process Based on Automated Monitoring Data

Application of UAV and GB-SAR in Mechanism Research and Monitoring of Zhonghaicun Landslide in Southwest China

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