Displacement prediction in colluvial landslides, Three Gorges Reservoir, China

Du, Juan; Yin, Kunlong; Lacasse, Suzanne

doi:10.1007/s10346-012-0326-8

Cited by 282 publications

(142 citation statements)

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“…In this application values have been selected according to the gathered data, the first threshold being just above the instrumental noise; the remaining have been set based on expert judgement waiting for a more robust calibration, which is possible only after at least a partial mobilization of the slope. Anyway, the system is open to any method for determining thresholds (Crosta and Agliardi, 2003;Du et al, 2013;Carlà et al, 2016a) and also to the use of other parameters (acceleration for example). Besides information from sensors and models, other information is obtained from meteorological and hydrological models (named "indicators").…”

Section: Early Warning Procedures Discussionmentioning

confidence: 99%

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Intrieri

Bardi

Fanti

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society.This research is part of the project "LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines".LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities.The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

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Section: Early Warning Procedures Discussionmentioning

confidence: 99%

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Intrieri

Bardi

Fanti

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…In the Three Gorges Reservoir region, landslides are the main type of geohazard, and they cause critical harm to individuals and property each year (Du et al, 2013;Yao et al, 2013;Lian et al, 2014;Cao et al, 2016). The displacement prediction of landslides is a major focus in the field of landslide research (Sassa et al, 2009;Du et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The displacement prediction of landslides is a major focus in the field of landslide research (Sassa et al, 2009;Du et al, 2013). Comprehensive analyses of landslide response and displacement predictions of landslide based on external factors are effective methods that rely on landslide deformation data.…”

Section: Introductionmentioning

confidence: 99%

“…These issues introduce uncertainty into the mathematical model and increase the difficulty of prediction. To overcome this and obtain the main error sources, a time series analysis of displacement is conducted by decomposing the monitoring data series into several components (Du et al, 2013). Then, the monitoring data series are simulated using the moving average method.…”

Section: Introductionmentioning

confidence: 99%

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Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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“…Corminas et al 2009;Du et al 2013). In principle, such approaches provide powerful tools in managing landslide risk, and there have been notable successes in which failure prediction has increased safety, especially in high-wall open-pit mines (e.g.…”

Section: Introductionmentioning

confidence: 99%

Patterns of movement in the Ventnor landslide complex, Isle of Wight, southern England

2014

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Abstract:The patterns of ground movement were monitored within a large, deep-seated landslide complex at Ventnor in southern England, between May 1998 and June 2002 using automated crackmeters, settlement cells and vibrating wire piezometers. It was found that the landslide maintains a state of marginal instability, such that it is subject to continual very slow deformation. Movement is primarily on a low-angled basal shear surface at >90 metres depth.The movement record shows a series of distinct deformation patterns that vary as groundwater conditions change. Continuous slow deformation occurs across the landslide complex at rates of between 5 -10 mm per year. The background pattern of movement does not appear to correlate with local porewater pressure. Periods of more rapid movement (reaching up to c. 34 mm per year during the monitoring period) were associated with a period of elevated groundwater, although the relationship between movement rate and porewater pressure was complex.The patterns of movement and the landslide geometry suggest that the likelihood of a rapid, catastrophic failure is low. Future episodes of faster movement are likely during periods when porewater pressures at the basal shear surface are elevated above a critical threshold. Whilst the resulting surface deformation damages the town, it is unlikely to occur rapidly without significant changes to the landslide hydrogeology or the stress state within the landslide. marginal instability, such that it is subject to continual very slow deformation. Movement is primarily on a low-angled basal shear surface at >90 metres depth. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationThe movement record shows a series of distinct deformation patterns that vary as groundwater conditions change. Continuous slow deformation occurs across the landslide complex at rates of between 5 -10 mm per year. The background pattern of movement does not appear to correlate with local porewater pressure. Periods of more rapid movement (reaching up to c. 34 mm per year during the monitoring period) were associated with a period of elevated groundwater, although the relationship between movement rate and porewater pressure was complex.The patterns of movement and the landslide geometry suggest that the likelihood of a rapid, catastrophic failure is low. Future episodes of faster movement are likely during periods when porewater pressures at the basal shear surface are elevated above a critical threshold. Whilst the resulting surface deformation damages the town, it is unlikely to occur rapidly without significant changes to the landslide hydrogeology or the stress state within the landslide.

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Displacement prediction in colluvial landslides, Three Gorges Reservoir, China

Cited by 282 publications

References 21 publications

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Patterns of movement in the Ventnor landslide complex, Isle of Wight, southern England

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