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A platform for research: civil engineering, architecture and urbanism
Landslide early warning, case studies from Southwest China
Abstract Landslides are one of the commonly natural disasters triggered by rainfalls, earthquakes, and human activities, which cause fatalities, damage to properties, and economic losses in the world. Early warning method may predict landslide occurrence and reduce the risk. In this paper, we propose a self-developed site-specific landslide early warning system (LEWS), which has been progressively designed and accomplished over the past decade in Southwest China. The warning model for the prediction of slope instability is focused on multiple thresholds, including deformation rate, rate increment and tangential angle related to surface displacement measurement. The debris-flow initiation is determined by critical rainfall thresholds integrated with the topographic and geological conditions at a catchment scale. The recent Xingyi rockslide, Baige debris slide and debris flows in Zoumaling catchment are selected to explain the specific process of landslide early warning in a real-time operating system. The performance of the presented warning system is evaluated by comparison with the inverse-velocity model (INV) and gradient model (SLO) in the prediction of slope failure time. The Receiver Operator Characteristic (ROC) analysis is used to prove its feasibility and reliability in the debris-flow initiation prediction. The presented warning model and system can be applied to other regions in landslide early warning and to be useful to mitigate landslide losses and damages.
Highlights Provide a successfully real-time landslide early warning system. Warning model and warning system can be applied in an emergency response to mitigate landslide losses. Comparing with the SLO, INV model and using ROC analysis to evaluate the performance of the warning system.
Landslide early warning, case studies from Southwest China
Abstract Landslides are one of the commonly natural disasters triggered by rainfalls, earthquakes, and human activities, which cause fatalities, damage to properties, and economic losses in the world. Early warning method may predict landslide occurrence and reduce the risk. In this paper, we propose a self-developed site-specific landslide early warning system (LEWS), which has been progressively designed and accomplished over the past decade in Southwest China. The warning model for the prediction of slope instability is focused on multiple thresholds, including deformation rate, rate increment and tangential angle related to surface displacement measurement. The debris-flow initiation is determined by critical rainfall thresholds integrated with the topographic and geological conditions at a catchment scale. The recent Xingyi rockslide, Baige debris slide and debris flows in Zoumaling catchment are selected to explain the specific process of landslide early warning in a real-time operating system. The performance of the presented warning system is evaluated by comparison with the inverse-velocity model (INV) and gradient model (SLO) in the prediction of slope failure time. The Receiver Operator Characteristic (ROC) analysis is used to prove its feasibility and reliability in the debris-flow initiation prediction. The presented warning model and system can be applied to other regions in landslide early warning and to be useful to mitigate landslide losses and damages.
Highlights Provide a successfully real-time landslide early warning system. Warning model and warning system can be applied in an emergency response to mitigate landslide losses. Comparing with the SLO, INV model and using ROC analysis to evaluate the performance of the warning system.
Landslide early warning, case studies from Southwest China
Ju, Nengpan (author) / Huang, Jian (author) / He, Chaoyang (author) / Van Asch, T.W.J. (author) / Huang, Runqiu (author) / Fan, Xuanmei (author) / Xu, Qiang (author) / Xiao, Yang (author) / Wang, Jue (author)
Engineering Geology ; 279
2020-11-11
Article (Journal)
Electronic Resource
English
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