Comparing landslide susceptibility models in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico

Paulín, Gabriel Legorreta; Pouget, S.; Bursik, Marcus; Quesada, Fernando Aceves; Contreras, Trevor A.

doi:10.1007/s11069-015-1960-y

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…On the other h within the VWT-AHP method, the judgment matrix is established by researchers, i ducing a notable element of subjectivity. Even though results with relatively high a racy have been obtained, to enhance the objectivity and precision of weights, altern methodologies such as regression models, decision trees, and artificial neural netw could be considered [123][124][125]. The critical infrastructures and artificial surfaces are primarily located in medium and high geo-environment vulnerability areas, particularly in the major cities in the study area (Figure 20).…”

Section: Geo-hazard Prevention Strategiesmentioning

confidence: 99%

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Huang,

Wu,

et al. 2023

Remote Sensing

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Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.

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Section: Geo-hazard Prevention Strategiesmentioning

confidence: 99%

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Huang,

Wu,

et al. 2023

Remote Sensing

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“…The model estimates the landslide probability between 0 and 1, and is fairly successful if the model is based on an adequate sample strategy, su cient samples, and a set of variables that are related to the processes that trigger landslides (Ayalew and Yamagishi 2005;Legorreta et al 2016;Yang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…By applying heuristic-geomorphological criteria, volcanic landforms were used to establish landslide frequency, landslide susceptibility, and landslide volumes in watersheds along the southwest ank of the volcano Pico de Orizaba, Mexico (Legorreta et al 2012(Legorreta et al , 2015. Also for Pico de Orizaba, three landslide susceptibility models (Stability Index MAPping, MLR, and multicriteria evaluation) were trained with in situ variables, and their results were compared and contrasted (Legorreta et al 2016). In the study, the MLR model was preferred over the other two because of its close match with the landslide inventory (79.81% producer's accuracy), and greater predictive capability with AUC of 0.831 and overall accuracy of 71.24 % (Legorreta et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Also for Pico de Orizaba, three landslide susceptibility models (Stability Index MAPping, MLR, and multicriteria evaluation) were trained with in situ variables, and their results were compared and contrasted (Legorreta et al 2016). In the study, the MLR model was preferred over the other two because of its close match with the landslide inventory (79.81% producer's accuracy), and greater predictive capability with AUC of 0.831 and overall accuracy of 71.24 % (Legorreta et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Modeling Spatial Landslide Probability in Volcanic Terrains Through Continuous Neighborhood Spatial Analysis and Multiple Logistic Regression in La Ciénega Watershed, Nevado de Toluca, Mexico

Miguel

Paulín

Bonifaz-Alfonzo

et al. 2021

Preprint

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Little work has been done on the effect of the pixel neighborhood information when modeling landslide susceptibility using Multiple Logistic Regression (MLR). This research uses in situ and neighborhood cartographic information to evaluate how pixel distance of sampling sites affects the precision and accuracy of the MLR landslide susceptibility model. Two landslide susceptibility models are used: MLR-in situ, calibrated and validated by using variables that are collected at the site of the sampling point; and MLR in combination with Continuous Neighborhood Spatial Analysis (CNSA) to incorporate a search radius to extract pixel values for each cartographic variable based on a distance ratio. La Ciénega watershed on the eastern flank of the volcano Nevado de Toluca is selected as a study area. Its climate, topography, geomorphology, and geology predispose it to episodic landslides. The resulting susceptibility maps are validated in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC), and they are compared with an inventory map in a contingency table; the MLR-CNSA model yields the better spatial prediction and representation of landslide susceptibility. The AUC evaluation indicates a predictive capability for the MLR-CNSA model of 0.969.

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“…Landslides are among the most frequent natural disasters in the world, especially in tropical regions, which are characterized by having deep and weathered soil profiles, steep slopes, and high precipitation (Cho, 2014). Understanding the mechanical behavior of soil-water system becomes relevant since landslides cause significant human and economic losses (Tarolli et al, 2011;Legorreta Paulin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Effects of Spatial Variability on Slope Reliability: A Hypothetical Case Study

Muñoz¹,

Neto²,

Ochoa³

2018

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Due to the complexity and uncertainty involved in mass movements, it is not possible to predict their occurrence with accuracy. This uncertainty is due to the space-time variability of physical soil parameters and processes, which determine the boundary conditions of the problem. In this study, we analyzed the influence of spatial variability of the parameters that determine soil resistance, the water retention curve and the unsaturated hydraulic conductivity function on the Factor of Safety of a hypothetical slope. To determinate the parameters to which the slope is more sensitive, we carried out a sensitivity analysis using the First-Order Second-Moment Method. We assumed that spatial variability follows normal and lognormal distribution, by using a methodology based on Monte Carlo Simulations and a Kriging process. The water flow equation is solved using numerical methods and the Factor of Safety was found with the Limit Equilibrium Method. According to the sensitivity analysis, the parameters that most affect the stability of the slope analyzed are cohesion, friction angle, air-entry value, and saturated hydraulic conductivity. When varying the air-entry value and cohesion, the probability distributions have very low dispersion, and mode has values similar to the deterministic values of Factor of Safety. The hydraulic conductivity variation results in that the values of mode move further away from the deterministic Factor of Safety as time progresses, increasing at the same time the dispersion. When all parameters are varied simultaneously, the behavior of the Factor of Safety is highly influenced by the hydraulic conductivity.

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Comparing landslide susceptibility models in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico

Cited by 15 publications

References 37 publications

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Modeling Spatial Landslide Probability in Volcanic Terrains Through Continuous Neighborhood Spatial Analysis and Multiple Logistic Regression in La Ciénega Watershed, Nevado de Toluca, Mexico

Effects of Spatial Variability on Slope Reliability: A Hypothetical Case Study

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