Discriminant analysis of the geomorphic characteristics and stability of landslide dams

Dong, Jia Jyun; Tung, Yu Hsiang; Chen, Chien Chih; Liao, Jo Nan; Pan, Yii‐Wen

doi:10.1016/j.geomorph.2009.04.004

Cited by 108 publications

(74 citation statements)

References 20 publications

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“…DA is a statistical method which explores the combination of independent variables that contribute to the presence and absence of landslide events (Reger 1979;Carrara 1983;Gorsevski et al 2000;Dong et al 2009). Stepwise DA enables the addition or removal of the independent variables based on the outcome of analysis.…”

Section: Discriminant Analysis (Da)mentioning

confidence: 99%

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Mahalingam

Olsen

O'Banion

2016

Geomatics, Natural Hazards and Risk

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Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.

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Section: Discriminant Analysis (Da)mentioning

confidence: 99%

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Mahalingam

Olsen

O'Banion

2016

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…types of failures). The model determines multiple combinations of employed independent variables (which are the discriminant functions) and selects the most significant combination (it is the combination that best discriminates categories of dependent variables) (Baeza et al 2010;Dong et al 2009). For example, if there are m categories of dependent variables, the possible combination of independent variables or discriminant functions will be m-1.…”

Section: Model Selectionmentioning

confidence: 99%

Deterministic slope failure hazard assessment in a model catchment and its replication in neighbourhood terrain

Acharya

Yatabe

Bhandary

et al. 2014

Geomatics, Natural Hazards and Risk

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In this work, we prepare and replicate a deterministic slope failure hazard model in small-scale catchments of tertiary sedimentary terrain of Niihama city in western Japan. It is generally difficult to replicate a deterministic model from one catchment to another due to lack of exactly similar geo-mechanical and hydrological parameters. To overcome this problem, discriminant function modelling was done with the deterministic slope failure hazard model and the DEM-based causal factors of slope failure, which yielded an empirical parametric relationship or a discriminant function equation. This parametric relationship was used to predict the slope failure hazard index in a total of 40 target catchments in the study area. From ROC plots, the prediction rate between 0.719-0.814 and 0.704-0.805 was obtained with inventories of September and October slope failures, respectively. This means September slope failures were better predicted than October slope failures by approximately 1%. The results show that the prediction of the slope failure hazard index is possible, even in a small catchment scale, in similar geophysical settings. Moreover, the replication of the deterministic model through discriminant function modelling was found to be successful in predicting typhoon rainfall-induced slope failures with moderate to good accuracy without any use of geo-mechanical and hydrological parameters.

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“…"LS" and "WS" stand for "landslide" and "watershed", respectively. 2008; Crosta and Clague, 2009;Dong et al, 2009;Nandi and Shakoor, 2009;Shrestha and Nakagawa, 2016), each of the natural dam cases has its own specificities related to the local context. Therefore, case studies are very important.…”

Section: Introductionmentioning

confidence: 99%

Formation, breaching and flood consequences of a landslide dam near Bujumbura, Burundi

Nibigira

Havenith

Archambeau

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper investigates the possible formation of a landslide dam on the Kanyosha River near Bujumbura, the capital of Burundi, as well as the interplay between the breaching of this landslide dam and the flooding along the river. We present an end-to-end analysis, ranging from the origin of the landslide up to the computation of flood waves induced by the dam breaching. The study includes three main steps. First, the mass movement site was investigated with various geophysical methods that allowed us to build a general 3-D model and detailed 2-D sections of the landslide. Second, this model was used for dynamic landslide process modelling with the Universal Distinct Element Code. The results showed that a 15 m high landslide dam may form on the river. Finally, a 2-D hydraulic model was set up to find out the consequences of the breaching of the landslide dam on flooding along the river, especially in an urban area located downstream. Based on 2-D maps of maximum water depth, flow velocity and wave propagation time, the results highlight that neglecting the influence of such landslide dams leads to substantial underestimation of flood intensity in the downstream area.

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Discriminant analysis of the geomorphic characteristics and stability of landslide dams

Cited by 108 publications

References 20 publications

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Deterministic slope failure hazard assessment in a model catchment and its replication in neighbourhood terrain

Formation, breaching and flood consequences of a landslide dam near Bujumbura, Burundi

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