Quantitative approches in landslide hazard analyses

1987 ◽  
Vol 69 (1) ◽  
pp. 43-53
Author(s):  
H. F. H. M. Mulder ◽  
Theodoor Wouterus Johannes Van Asch
Keyword(s):  
Landslide Hazard

Evaluation of Landslide Hazard Degree Based On the Normal Cloud Model

2017 ◽  
Vol 10 (01) ◽  
pp. 88-94
Author(s):  
CHEN DONGHUI ◽  
XU PEIHUA ◽  
ZHANG WEN ◽  
CHEN JIANPING ◽  
SONG SHENGYUAN ◽  
...  
Keyword(s):  
Cloud Model ◽  
Landslide Hazard ◽  
Hazard Degree

REGIONAL PROBABILISTIC LANDSLIDE HAZARD ASSESSMENT FOR THE ENGURI DAM (JVARI, GEORGIA)

2016 ◽  
Author(s):  
Maria Diletta Acciaro ◽  
◽  
John S. Gierke ◽  
Thomas Oommen
Keyword(s):  
Hazard Assessment ◽  
Landslide Hazard ◽  
Landslide Hazard Assessment

scholarly journals From scenario-based seismic hazard to scenario-based landslide hazard: fast-forwarding to the future via statistical simulations

2021 ◽  
Author(s):  
Luigi Lombardo ◽  
Hakan Tanyas
Keyword(s):  
Ground Motion ◽  
Landslide Susceptibility ◽  
Additive Model ◽  
Landslide Hazard ◽  
Northridge Earthquake ◽  
Ground Acceleration ◽  
Temporal Dimension ◽  
Earthquake Scenario ◽  
The Mean ◽  
Ground Motion Scenarios

AbstractGround motion scenarios exists for most of the seismically active areas around the globe. They essentially correspond to shaking level maps at given earthquake return times which are used as reference for the likely areas under threat from future ground displacements. Being landslides in seismically actively regions closely controlled by the ground motion, one would expect that landslide susceptibility maps should change as the ground motion patterns change in space and time. However, so far, statistically-based landslide susceptibility assessments have primarily been used as time-invariant.In other words, the vast majority of the statistical models does not include the temporal effect of the main trigger in future landslide scenarios. In this work, we present an approach aimed at filling this gap, bridging current practices in the seismological community to those in the geomorphological and statistical ones. More specifically, we select an earthquake-induced landslide inventory corresponding to the 1994 Northridge earthquake and build a Bayesian Generalized Additive Model of the binomial family, featuring common morphometric and thematic covariates as well as the Peak Ground Acceleration generated by the Northridge earthquake. Once each model component has been estimated, we have run 1000 simulations for each of the 217 possible ground motion scenarios for the study area. From each batch of 1000 simulations, we have estimated the mean and 95% Credible Interval to represent the mean susceptibility pattern under a specific earthquake scenario, together with its uncertainty level. Because each earthquake scenario has a specific return time, our simulations allow to incorporate the temporal dimension into any susceptibility model, therefore driving the results toward the definition of landslide hazard. Ultimately, we also share our results in vector format – a .mif file that can be easily converted into a common shapefile –. There, we report the mean (and uncertainty) susceptibility of each 1000 simulation batch for each of the 217 scenarios.

Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China

2021 ◽  
Author(s):  
Wei Xie ◽  
Wen Nie ◽  
Pooya Saffari ◽  
Luis F. Robledo ◽  
Pierre-Yves Descote ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hazard Assessment ◽  
Landslide Hazard ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Landslide Hazard Assessment

GIS-based landslide hazard assessment: an overview

2005 ◽  
Vol 29 (4) ◽  
pp. 548-567 ◽  
Author(s):  
Wang Huabin ◽  
Liu Gangjun ◽  
Xu Weiya ◽  
Wang Gonghui
Keyword(s):  
Hazard Assessment ◽  
Landslide Hazard ◽  
Integrated System ◽  
Process Models ◽  
Land Utilization ◽  
Property A ◽  
Landslide Hazard Assessment ◽  
Landslide Hazards ◽  
Digital Elevation ◽  
Slope Management

In recent years, landslide hazard assessment has played an important role in developing land utilization regulations aimed at minimizing the loss of lives and damage to property. A variety of approaches has been used in landslide assessment and these can be classified into qualitative factor overlay, statistical models, geotechnical process models, etc. However, there is little work on the satisfactory integration of these models with geographic information systems (GIS) to support slope management and landslide hazard mitigation. This paper deals with several aspects of landslide hazard assessment by presenting a focused review of GIS-based landslide hazard assessment: it starts with a framework for GIS-based assessment of landslide hazard; continues with a critical review of the state of the art in using GIS and digital elevation models (DEM) for mapping and modelling landslide hazards; and concludes with a description of an integrated system for effective landslide hazard assessment and zonation incorporating artificial intelligence and data mining technology in a GIS-based framework of knowledge discovery.

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