Methodology for quantitative landslide risk analysis in residential projects

Isaza-Restrepo, Paola Andrea; Carvajal, Hernán Eduardo Martínez; Montoya, César Augusto Hidalgo

doi:10.1016/j.habitatint.2015.12.012

Cited by 12 publications

(9 citation statements)

References 12 publications

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“…1. Slope and elevation directly determine the stress distribution of a highway slope, larger slope and elevation will lead to higher potential energy, so that weak structural plane will be exposed easily and the highway slope will suffer from instability [13,35].…”

Section: Impact Factors Of Hldsmentioning

confidence: 99%

“…Researches on landslide susceptibility mapping (LSM) in China mainly focused on the Wenchuan, Yushu and Ya'an earthquake areas, the Three Gorges Reservoir areas, the areas affected by typhoons and loess areas; researches abroad China mainly focused on the Medellin areas (Columbia), Kyushu areas (Japan) and some areas in Italy [13][14][15]. The modeling methods implemented to LSM mainly included the statistical prediction models, i.e., Logistic regression method (LR), decision tree method, analytical hierarchy process (AHP), deterministic coefficient method and multivariate adaptive regression spline model (MARSplines), and the machine learning models, i.e., artificial neural network (ANN), support vector machine (SVM), neuro-fuzzy technique, decision tree model and Bayesian network (BN), some scholars also conducted comparison researches on multiple modeling methods [11,[16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

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Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

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Section: Impact Factors Of Hldsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

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“…Un peligro (evento) combinado con una probabilidad determinan la amenaza P [T], la cual combinada con las consecuencias expresan el riesgo (R) [17]. La vulnerabilidad está representada en las consecuencias, como el valor de la pérdida u(C) debido a la ocurrencia de un evento catastrófico [18]:…”

Section: Metodologíaunclassified

Evaluación del riesgo de deslizamientos superficiales para proyectos de infraestructura: caso de análisis en vereda El Cabuyal

et al. 2018

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Este artículo presenta un análisis y evaluación cuantitativa de la amenaza, la vulnerabilidad y el riesgo asociados a deslizamientos superficiales en una subcuenca del Valle de Aburrá (Colombia). Se integra el modelo TRIGRS mediante una simulación de Montecarlo para la estimación de la probabilidad de falla en la subcuenca empleando una altura del nivel freático como factor desencadenante, que junto con su probabilidad de ocurrencia anual permiten la evaluación de la amenaza. Se emplea un modelo que cuantifica la vulnerabilidad física de estructuras basándose en la intensidad del deslizamiento y la resistencia del elemento expuesto. El riesgo anual se estima para una vivienda del sitio de estudio como el producto de la amenaza y el valor de la pérdida dado el evento catastrófico, considerando la vulnerabilidad. La metodología empleada se enfoca en el deslizamiento ocurrido el 26 de octubre de 2016 en un talud dentro del sitio de estudio que afectó la vivienda que es objeto de análisis.

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“…One of the most effective measures for risk prevention and mitigation is the evaluation of both the hazard of the phenomenon and of the exposure and vulnerability of the elements at risk [17]. In Colombia, some studies have been carried out that evaluate risk assessment and reduction [7,[18][19][20], but there are many more that evaluate hazard or susceptibility. For hazard, there are numerous deterministic and probabilistic methods, the latter of which are generally the most applied for extensive areas due to the lack of precise and exhaustive data in such areas.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

Herrera-Coy,

Calderón,

Herrera-Pérez

et al. 2023

Remote Sensing

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Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one of the regions with the highest concentration of phenomena, which makes its study a priority. An inventory and detailed analysis of 2506 landslides has been carried out, in which five basic typologies have been differentiated: avalanches, debris flows, slides, earth flows and creeping areas. Debris avalanches and debris flows occur mainly in metamorphic materials (phyllites, schists and quartz-sandstones), areas with sparse vegetation, steep slopes and lower sections of hillslopes; meanwhile, slides, earth flows and creep occur in Cretaceous lutites, crop/grass lands, medium and low slopes and lower-middle sections of the hillslopes. Based on this analysis, landslide susceptibility models have been made for the different typologies and with different methods (matrix, discriminant analysis, random forest and neural networks) and input factors. The results are generally quite good, with average AUC-ROC values above 0.7–0.8, and the machine learning methods are the most appropriate, especially random forest, with a selected number of factors (between 6 and 8). The degree of fit (DF) usually shows relative errors lower than 5% and success higher than 90%. Finally, an integrated landslide susceptibility map (LSM) has been made for shallower and deeper types of movements. All the LSM show a clear zonation as a consequence of the geological control of the susceptibility.

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Methodology for quantitative landslide risk analysis in residential projects

Cited by 12 publications

References 12 publications

Susceptibility mapping and zoning of highway landslide disasters in China

Susceptibility mapping and zoning of highway landslide disasters in China

Evaluación del riesgo de deslizamientos superficiales para proyectos de infraestructura: caso de análisis en vereda El Cabuyal

Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

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