Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach

Zhou, Guangpeng; Esaki, Taito; Mitani, Yasuhiro; Mori, J.

doi:10.1016/s0013-7952(02)00241-7

Cited by 165 publications

(79 citation statements)

References 3 publications

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“…Practical steps for calibrating parameters often start with random combinations of parameters (e.g. Duan and Grant, 2000;Zhou et al, 2003). Model performance estimators are used to evaluate model outcomes, thus, acting as sorters to retrieve optimal simulations.…”

Section: Shalstab Model and Operationmentioning

confidence: 99%

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Optimal estimator for assessing landslide model performance

Huang

Kao

2006

Hydrol. Earth Syst. Sci.

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Abstract. The commonly used success rate (SR) in evaluating cell-based landslide model performance is based on the ratio of successfully predicted landslide sites over total actual landslide sites without considering the performance in predicting stable cells. We proposed a modified SR (MSR), in which the performance of stable cell prediction is included. The advantage of MSR is to avoid over-and under-prediction while upholding the stable sensitivity throughout all simulated cases. Stochastic analyses are conducted by using artificial landslide maps and simulations with a full range of performances (from worst to perfect) in both stable and unstable cell predictions. Stochastic analyses reveal mathematical responses of estimators to various model results in calculating performance. The Kappa method, which is commonly used for satellite image analysis, is improper for landslide modeling giving inconsistent performance when landslide coverage changes. To examine differences among SR and MSR in real model application, we applied the SHALSTAB model onto a mountainous watershed in Taiwan. Case study shows that stable and unstable cell predictions are inter-exclusive in SHALSTAB model. The optimal estimator should compromise landslide over-and under-prediction. According to our 4000 simulations, the best simulation generated by MSR projects 83 hits over 131 actual landslide sites while the unstable cells cover only 16% of the studied watershed. By contrast, despite the fact that the best simulation deduced from SR projects 120 hits over 131 actual landslide sites, this high performance is only obtained when unstable cells cover an incredibly high landslide cover (∼75%) of the entire watershed exhibiting a significant landslide over-prediction.

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Section: Shalstab Model and Operationmentioning

confidence: 99%

“…Practical steps for calibrating parameters often start with random combinations of parameters (e.g. Duan and Grant, 2000;Zhou et al, 2003). With MSR serving as a likelihood measure in landslide modeling, we are able to retrieve the best simulation out of abundant model outcomes (Huang et al, 2006).…”

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confidence: 99%

Optimal estimator for assessing landslide model performance

Huang

Kao

2006

Hydrol. Earth Syst. Sci.

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“…Su representación cartográfica y el análisis se llevaron a cabo e incorporaron en los SIG (Peark et al, 2005; Washington State Department of Natural Resources, 2006;Kalderon-Asael et al, 2008;Hervás y Bobrowsky, 2009;Guzzetti et al, 2009;Blahut et al, 2010;Wenkey et al, 2011). Por su parte, la evaluación de la susceptibilidad a deslizamientos y su zonificación se ha abordado de manera heurística, estadística o determinística (Dai et al, 2002;Clerici et al, 2002;Zhou et al, 2003;Metternicht et al, 2005;Demoulin y Chung, 2007;Castellanos-Abella y Van Westen, 2008). Asimismo se han hecho esfuerzos para probar y validar los modelos de susceptibilidad de deslizamientos de tierras (Montgomery y Dietrich, 1994;Pack et al, 1997Pack et al, , 1998Pack et al, , 2001Morrissey et al, 2001;Dietrich et al, 2001;Borga et al, 2002aBorga et al, , 2002bZaitchik et al, 2003;Wawer y Nowocień, 2003;Chinnayakanahalli et al, 2003;Lan et al, 2004;Winsemius et al, 2005;Legorreta-Paulín y Bursik, 2008).…”

Section: Antecedentesunclassified

Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos en el flanco SW del volcán Pico de Orizaba, Puebla-Veracruz

Paulín

Bursik

Pouget

et al. 2014

BSGM

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Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos 343 ResumenEste artículo provee un panorama general de la investigación en curso del Instituto de Geografía de la UNAM, que busca crear una metodología integral para el desarrollo de cartografía de inventarios multitemporales, de susceptibilidad y estimación de volúmenes de deslizamientos mediante el uso de Sistemas de Información Geográfica (SIG). La metodología se establece para terrenos volcáni-cos, donde deslizamientos superficiales y flujos de escombros son frecuentes a lo largo de las laderas y los sistemas fluviales de los volcanes. La cuenca del Río Chiquito-Barranca del Muerto en el flanco suroeste del volcán Pico de Orizaba se ha seleccionado como caso de estudio para caracterizar las áreas que son propensas a la inestabilidad de las laderas en terrenos volcánicos. La ocurrencia de deslizamientos se determinó a través de un inventario de deslizamientos creado a partir de fotos aéreas multitemporales y trabajo de campo. En este estudio se modela (simula) la susceptibilidad a deslizamientos usando un modelo cartográfico-hidrológico (Stability Index MAPping (SINMAP)) y un modelo estadístico (Regresión Logística Múltiple (RLM)). Ambos modelos están integrados dentro de la aplicación LOGISNET (desarrollado mediante el uso de macros (AML) del sistema de información geográfica (SIG) ArcInfo Workstation. Su implementación mostró que ambos modelos poseen un grado moderado de concordancia con el inventario de deslizamientos, prefiriéndose la RLM para futuros análisis por su flexibilidad de incluir otras variables. Para la estimación de volúmenes de materiales aportados por los deslizamientos a los cauces, se seleccionaron los deslizamientos superficiales. El área y volumen de deslizamientos superficiales individuales fueron obtenidos en campo y a través de la base de datos del inventario de deslizamientos, para establecer una relación empírica: área-volumen. La relación se utilizó para el uso de una ley de potencias con la que se estimó el volumen total de deslizamientos de tierra en el área de estudio. Los resultados son importantes para comprender la evolución a largo plazo del sistema fluvial en el flanco suroeste del volcán Pico de Orizaba, y apoyar el desarrollo de una metodología útil para la elaboración de inventarios, el análisis de la susceptibilidad y estimación de volúmenes de deslizamientos en terrenos volcánicos.

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“…Ohlmacher and Davis (2003) applied the logistic regression model to landslide susceptibility mapping. Shou and Wang (2003) and Zhou et al (2003) applied geotechnical and safety factor models indicate the landslide hazards. Other techniques (Lee 2007;Vijith & Madhu 2007) have been applied to landslide mapping Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island, Malaysia

Elmahdy

Marghany

Mohamed

2014

Geomatics, Natural Hazards and Risk

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In the current study, Penang Island, which is one of the several mountainous areas in Malaysia that is often subjected to landslide hazard, was chosen for further investigation. A multi-criteria Evaluation and the spatial probability weighted approach and model builder was applied to map and analyse landslides in Penang Island. A set of automated algorithms was used to construct new essential geological and morphometric thematic maps from remote sensing data. The maps were ranked using the weighted probability spatial model based on their contribution to the landslide hazard. Results obtained showed that sites at an elevation of 100-300 m, with steep slopes of 10 -37 and slope direction (aspect) in the E and SE directions were areas of very high and high probability for the landslide occurrence; the total areas were 21.393 km 2 (11.84%) and 58.690 km 2 (32.48%), respectively. The obtained map was verified by comparing variogram models of the mapped and the occurred landslide locations and showed a strong correlation with the locations of occurred landslides, indicating that the proposed method can successfully predict the unpredictable landslide hazard. The method is time and cost effective and can be used as a reference for geological and geotechnical engineers.

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Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach

Cited by 165 publications

References 3 publications

Optimal estimator for assessing landslide model performance

Optimal estimator for assessing landslide model performance

Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos en el flanco SW del volcán Pico de Orizaba, Puebla-Veracruz

Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island, Malaysia

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