Techniques for evaluating the performance of landslide susceptibility models

Frattini, Paolo; Crosta, Giovanni B.; Carrara, A.

doi:10.1016/j.enggeo.2009.12.004

Cited by 402 publications

(237 citation statements)

References 51 publications

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“…The percentage of landslide occurrence (POLO) and its cumulative (CPOLO) are commonly used as a good indicator to measure the performance of LSM: The steeper the CPOLO curve, the better the capability of LSM to predict landslides and to validate LSMs (e.g., Chung and Fabbri 2003;Frattini et al 2010;Mezughi et al 2011;Oh and Lee 2011). We follow this procedure to derive a success rate curve by calculating the LSM of all cells using Eq.…”

Section: Resultsmentioning

confidence: 99%

A new region-based preparatory factor for landslide susceptibility models: the total flux

Liu

Luo

Chen³

et al. 2015

Landslides

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This paper presents a new region-based preparatory factor, total flux (TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides.

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Section: Resultsmentioning

confidence: 99%

A new region-based preparatory factor for landslide susceptibility models: the total flux

Liu

Luo

Chen³

et al. 2015

Landslides

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“…ROC curves relate the proportion of unstable terrain units correctly classified as susceptible (true positive rate) with the proportion of stable terrain units classified as susceptible (false positive rate) (Frattini et al, 2010).…”

Section: Landslide Susceptibility Modelsmentioning

confidence: 99%

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Epifânio

Zêzere

Neves

2014

Journal of Sea Research

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The coastal zone of Lourinhã (located in Central Portugal) is characterized by beach-cliff systems, where beaches are narrow and cliffs have notorious slope instability. These cliffs evolve by different types of landslides, which are one of the main sources of natural hazard and risk in this coastal region. In this work, aerial photo interpretation and a systematic field survey were performed in order to obtain an inventory of landslides of the following types: rotational slides, translational slides and debris flows. The entire coast was then split into 261 terrain mapping units. For each unit, landslide predisposing factors were derived and classified: cliff elevation, slope angle (maximum, mean and standard deviation), potential solar radiation, slope curvature (profile and plan), lithological units and geologic structure. The predictive susceptibility models were computed for each type of landslide using a bi-variate statistical method -the Information Value Method. The degree of fit and the predictive capacity of the models were assessed using the Effectiveness Ratio, the standard Receiver Operator Characteristic curves and the respective Area Under Curve. Results show that each landslide typology occurs in particular terrain conditions. Individual susceptibility models evidence better predictive capacity than susceptibility model for total landslides.

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“…Therefore the PSS is widely applied for the validation and evaluation of predictive models in hazard assessment and risk management (Beguería, 2006). Examples for this are the 20 validation of landslide susceptibility models (Leonarduzzi et al, 2017;Gariano et al, 2015;Frattini et al, 2010) or avalanche hazard (Purves et al, 2003). As a second overall accuracy measure we apply the Heidke Skill Score HSS (Heidke, 1926), also referred to as Kappa.…”

Section: Validation Methodsmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Preprint

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Abstract. Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were 10 developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such areawide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial 15 planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-theart numerical avalanche simulations.In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, 20 that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-25 based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 -30 years return period) and extreme (100 -300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and 30 snow data are available.Nat. Hazards Earth Syst. Sci. Discuss., https://doi

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Techniques for evaluating the performance of landslide susceptibility models

Cited by 402 publications

References 51 publications

A new region-based preparatory factor for landslide susceptibility models: the total flux

A new region-based preparatory factor for landslide susceptibility models: the total flux

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

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