scholarly journals Influences of the Shadow Inventory on a Landslide Susceptibility Model

2018 ◽  
Vol 7 (9) ◽  
pp. 374 ◽  
Author(s):  
Cheng-Chien Liu ◽  
Wei Luo ◽  
Hsiao-Wei Chung ◽  
Hsiao-Yuan Yin ◽  
Ke-Wei Yan
Keyword(s):  
Landslide Susceptibility ◽  
Geometric Mean ◽  
Warning System ◽  
Landslide Hazard ◽  
Mountainous Areas ◽  
System A ◽  
Susceptibility Model ◽  
Hazard And Risk ◽  
Optical Imagery ◽  
Landslide Susceptibility Index

A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system.

scholarly journals Susceptibility analysis of landslide in Chittagong City Corporation Area, Bangladesh

2015 ◽  
Vol 4 (2) ◽  
pp. 157-181 ◽  
Author(s):  
Sourav Das ◽  
Debasish Roy Raja
Keyword(s):  
Emergency Preparedness ◽  
Landslide Susceptibility ◽  
Assessment Tool ◽  
Risk Mitigation ◽  
Landslide Hazard ◽  
Mitigation Strategies ◽  
Gis And Remote Sensing ◽  
City Life ◽  
Critical Facilities ◽  
Landslide Susceptibility Index

In Chittagong city, landslide phenomena is the most burning issue which causes great problems to the life and properties and it is increasing day by day and becoming one of the main problems of city life. On 11 June 2007, a massive landslide happened in Chittagong City Corporation (CCC) area, a large number of foothill settlements and slums were demolished; more than 90 people died and huge resource destruction took place. It is therefore essential to analyze the landslide susceptibility for CCC area to prepare mitigation strategies as well as assessing the impacts of climate change. To assess community susceptibility of landslide hazard, a landslide susceptibility index map has been prepared using analytical hierarchy process (AHP) model based on geographic information system (GIS) and remote sensing (RS) and its susceptibility is analyzed through community vulnerability assessment tool (CVAT). The major findings of the research are 27% of total CCC area which is susceptible to landslide hazard and whereas 6.5 sq.km areas are found very highly susceptible. The landslide susceptible areas of CCC have also been analyzed in respect of physical, social, economic, environmental and critical facilities and it is found that the overall CCC area is highly susceptible to landslide hazard. So the findings of the research can be utilized to prioritize risk mitigation investments, measures to strengthen the emergency preparedness and response mechanisms for reducing the losses and damages due to future landslide events. DOI: http://dx.doi.org/10.3126/ije.v4i2.12635 International Journal of Environment Vol.4(2) 2015: 157-181

scholarly journals A Hybrid Landslide Susceptibility Model Obtained from Different Models (Raster or Vector, Expert or Semi-Expert) with the Correct Parameter

2021 ◽  
Author(s):  
Mehmet Emin Cihangir
Keyword(s):  
Landslide Susceptibility ◽  
High Efficiency ◽  
Field Analysis ◽  
Landslide Inventory ◽  
Depletion Zone ◽  
Qualitative And Quantitative ◽  
Single Output ◽  
Susceptibility Model ◽  
Triggering Conditions ◽  
Hazard And Risk

Abstract This study aims to determine how to choose the correct parameter for a specific study area in landslide susceptibility and how it gives results in vector or raster-based models. In the literature, factor parameters of landslide preparing and triggering conditions are used deliberately or randomly in raster or vector-based models. In this study, the landslide inventory was analyzed together with geological, topographic-morphological, environmental, and triggering parameters, and the parameters specific to the study area and its scale were decided. In order to obtain high efficiency from the models, the parameter data were taken from the landslide depletion zone. Raster-based models and vector-based models were created according to qualitative and quantitative approaches. Model outputs resulted in close Roc Curve results ranging from 0.79 to 0.92. The study area was divided into slope units and then the model output data were transferred to these units. In order to make the result easier to use, the units obtained according to the result of each model were combined, thus a single map output was obtained from 5 different raster and vector-based models. Overall, this study presents 1) the importance of the use of landslide inventory and how to use the inventory. 2) Parameters should be selected according to field analysis and field-scale rather than randomly. 3) By combining raster and vector-based on landslide susceptibility studies, make it easier to use as a base map in hazard and risk studies with a single output.

scholarly journals Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories

2009 ◽  
Vol 9 (3) ◽  
pp. 673-686 ◽  
Author(s):  
D. B. Kirschbaum ◽  
R. Adler ◽  
Y. Hong ◽  
A. Lerner-Lam
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Hazard ◽  
Soil Conditions ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
Soil Variables ◽  
Topographic Slope ◽  
Predictive Skill ◽  
Landslide Event ◽  
Susceptibility Model

Abstract. Most landslide hazard assessment algorithms in common use are applied to small regions, where high-resolution, in situ, observables are available. A preliminary global landslide hazard algorithm has been developed to estimate areas of potential landslide occurrence in near real-time by combining a calculation of landslide susceptibility with satellite derived rainfall estimates to forecast areas with increased potential for landslide conditions. This paper presents a stochastic methodology to compare this new, landslide hazard algorithm for rainfall-triggered landslides with a newly available inventory of global landslide events, in order to determine the predictive skill and limitations of such a global estimation technique. Additionally, we test the sensitivity of the global algorithm to its input observables, including precipitation, topography, land cover and soil variables. Our analysis indicates that the current algorithm is limited by issues related to both the surface-based susceptibility map and the temporal resolution of rainfall information, but shows skill in determining general geographic and seasonal distributions of landslides. We find that the global susceptibility model has inadequate performance in certain locations, due to improper weighting of surface observables in the susceptibility map. This suggests that the relative contributions of topographic slope and soil conditions to landslide susceptibility must be considered regionally. The current, initial forecast system, although showing some overall skill, must be improved considerably if it is to be used for hazard warning or detailed studies. Surface and remote sensing observations at higher spatial resolution, together with improved landslide event catalogues, are required if global landslide hazard forecasts are to become an operational reality.

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.

scholarly journals Applicability of Susceptibility Model for Rock and Loess Earthquake Landslides in the Eastern Tibetan Plateau

2021 ◽  
Vol 13 (13) ◽  
pp. 2546
Author(s):  
Xinyi Guo ◽  
Bihong Fu ◽  
Jie Du ◽  
Pilong Shi ◽  
Qingyu Chen ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Disaster Relief ◽  
Coupling Model ◽  
Scientific Data ◽  
Eastern Tibetan Plateau ◽  
Loess Landslide ◽  
Rock Landslide ◽  
Susceptibility Model ◽  
Prediction Capability ◽  
Better Than

It is crucial to explore a suitable landslide susceptibility model with an excellent prediction capability for rapid evaluation and disaster relief in seismic regions with different lithological features. In this study, we selected two typical seismic events, the Jiuzhaigou and Minxian earthquakes, which occurred in the Alpine karst and loess regions, respectively. Eight influencing factors and five models were chosen to calculate the susceptibility of landslide, including the information (I) model, certainty factor (CF) model, logistic regression (LR) model, I + LR coupling model, and CF + LR coupling model. Then, the accuracy and the landslide susceptibility distribution of these models were assessed by the area under curve (AUC) and distribution criteria. Finally, the model with high accuracy and good applicability for the rock landslide or loess landslide regions was optimized. Our results showed that the accuracy of the coupling model is higher than that of the single models. Except for the LR model, the landslide susceptibility distribution for the above-mentioned models is consistent with universal cognition. The coupling models are generally better than their single models. Among them, the I + LR model can obtain the best comprehensive results for assessing the distribution and accuracy of both rock and loess landslide susceptibility, which is helpful for disaster relief and policy-making, and it can also provide useful scientific data for post-seismic reconstruction and restoration.

Effectiveness of Truck Rollover Warning Systems

2001 ◽  
Vol 1779 (1) ◽  
pp. 134-140 ◽  
Author(s):  
Derek Baker ◽  
Rob Bushman ◽  
Curtis Berthelot
Keyword(s):  
North America ◽  
Warning System ◽  
Warning Systems ◽  
Commercial Vehicles ◽  
Decision Criteria ◽  
Warning Messages ◽  
System A ◽  
Different Types ◽  
Rollover Warning

Different types of intelligent rollover system deployed by road agencies across North America are investigated. The importance of weight is addressed for maximum effectiveness of rollover warning messages for commercial vehicles in a potential rollover situation on sharp curves or exit ramps. The type of information that may be used to activate a rollover is discussed to analyze the number of correctly warned vehicles compared with the number of false warnings generated by the rollover warning system. A case study of the effectiveness of an intelligent rollover system is presented. On the basis of this case study, it was found that speed-based rollover warning systems generated anywhere from 44 percent to 49 percent more false rollover warnings for commercial vehicles than did rollover warning systems that employed weight information in the rollover decision criteria.

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