Recommendations for the quantitative analysis of landslide risk

Corominas, Jordi; Westen, C.J. van; Frattini, Paolo; Cascini, Leonardo; Malet, Jean‐Philippe; Fotopoulou, Stavroula; Catani, Filippo; Eeckhaut, Miet Van Den; Mavrouli, Olga; Agliardi, Federico; Pitilakis, Kyriazis; Winter, M G; Pastor, M.; Ferlisi, Settimio; Tofani, Veronica; Hervás, Javier; Smith, Jessica

doi:10.1007/s10064-013-0538-8

Cited by 586 publications

(629 citation statements)

References 277 publications

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“…The magnitude (or intensity) of landslides depends on the landslide type and several proxies can be used for its quantification such as the landslide dimensions (area, volume, travel distance) or velocity (Corominas et al, 2014). Frequencymagnitude relationships for different locations and landslide types are frequently considered as a proxy for landslide magnitude (van den Eeckhaut et al, 2007;Florsheim and Nichols, 2013;Malamud et al, 2004;Schlögel et al, 2011;Guzzetti et al, 2006).…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

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Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Schlögel

Malet

Reichenbach

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. In the paper we analyse a multi-date landslide inventory prepared for a mountainous area affected by several landslide types with different degrees of activity, we attempt to quantify the uncertainties associated to the mapping, we measure the evolution of morphological indicators and estimate landslide activity and temporal occurrence. The inventory, covering the period 1956-2010, is prepared for the middle section of the Ubaye valley (southern French Alps) based on the analysis of multi-source documents (geomorphological maps, historical reports of landslide events, field surveys, orthophotographs and SAR (synthetic aperture radar) satellite images). The uncertainties derived from the expert interpretation of different sources of information, the landslide morphological features and the affected land covers are taken into account in relation to the source documents.Morphological indicators are calculated to describe quantitatively the evolution of the landslides (length, area, relative elevation, runout distance). Frequency-area density functions are computed to estimate the changes in the landslide distributions and a Poisson model is used to estimate the probability of reactivation of the observed landslides and the occurrence of new failures. The proposed multi-date inventory and the associated statistics provide additional information to the event catalogue managed by the local policy makers.

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Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

“…They are also useful to investigate the distribution, types and patterns of landslides in relation to geomorphology, lithology, land cover, tectonic settings and hydrogeological conditions (Guzzetti et al, 1996;Corominas et al, 2014;Günther et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Schlögel

Malet

Reichenbach

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…Landslide risk can be expressed with an equation that includes the probability of occurrence multiplied by the value of the element at risk of landside, and to calculate the probability of landslide occurrence, the frequency of landslide occurrence 30 should be estimated first (Corominas et al, 2014). However, landslide investigation data are not yet sufficiently accumulated Nat.…”

Section: Frequency Of Landslide Occurrencementioning

confidence: 99%

“…To reflect the spatial probability data from landslide susceptibility classification, the landslide hazard map produced by the Korea Forest Service was used (Korea Forest Service, 2012), and temporal probability was incorporated into this study by considering the frequency of landslide occurrence estimated from the landslide inventory data. 20 For estimation of the frequency of landslide occurrence, a descriptor of relative temporal frequencies was adopted as suggested by (Corominas and Moya, 2008) to conduct the study on a regional scale, and the number of landslide events was analysed along with the landslide hazard grades. Despite the limitation on the accumulated landslide inventory data over the study region, the frequency of landslide occurrence was estimated by establishing a rainfall threshold.…”

Section: Introduction 25mentioning

confidence: 99%

Risk-based analysis of monitoring time intervals for landslide prevention

Lee

Kil

et al. 2017

Preprint

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Abstract.Landslide is one of the most dangerous disasters in terms of occurrence frequency and damage severity that result in loss of human life and social infrastructure. Monitoring methods based on low temporal resolution instruments such as an inclinometer or piezometer can be an effective and cost-efficient solution. The objective of this research is to analyse monitoring time 15 intervals for low temporal resolution methods based on a risk study and to propose a plan for periodic landslide monitoring along with the different landslide hazard areas by considering the risk reduction effect. For this purpose, an equation for the probability of landslide occurrence was denoted by the concept of reliability, and the monitoring time interval was analysed quantitatively by calculating the average probability of landslide occurrence. To identity the frequency of landslide occurrence, a unit of relative temporal frequencies was adopted, and it was estimated by establishing rainfall threshold. As a case study 20 site, Pyeongchang County was selected, where landslide inventory data are available and an increase in population and infrastructure has been observed since Pyeongchang became the host city of the 2018 winter Olympic games. The result demonstrates that the appropriate monitoring intervals can be determined by calculating the average probability of landslide occurrence, and resources can then be allocated efficiently for landslide prevention.

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“…This grid represents the landslide susceptibility with a numerical value assigned to each cell location. Also, this component provides the facility to produce the source files to draw Receiver Operating Characteristic (ROC) curves (Corominas et al 2013) and success rate curves to assess the accuracy of the susceptibility model with respect to the Landslide Inventory distribution. 4 The fourth and final component can be used to visualize the ROC curves and success rate curves.…”

Section: Fig 21 Lsdm Toolbarmentioning

confidence: 99%

ArcGIS V.10 Landslide Susceptibility Data Mining Add-in Tool Integrating Data Mining and GIS Techniques to Model Landslide Susceptibility

Palamakumbure

Stirling

Flentje

et al. 2015

Engineering Geology for Society and Territory - Volume 2

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Landslide susceptibility modeling is an essential early step towards managing landslide risk. A minimum of $4.8 million is lost due to landslide related damages every year in Illawara region of Australia. At present, Data mining and knowledge discovery techniques are becoming popular in building landslide susceptibility models due to their enhanced predictive performances. Until now, the lack of tools to undertake data extraction and making the predictions have limited the applicability of this novel technique in landslide model building. This paper discusses the development of the LSDM (Landslide Susceptibility Data Mining) toolbar which was designed to utilize machine learning techniques within a GIS environment by coupling GIS and data mining software (See5) capabilities. The software development kit available with ArcGIS v.10 has been utilized in developing the toolbar add-in. The fundamental tasks; data preparation, model optimizing, derivation of decision trees, predictions and validation are all performed using the individual controls available in the toolbar. This tool automates the entire model building process and in preparation of training data and producing outcomes that are compliant with both national and international Landslide Risk management guidelines. Landslide susceptibility modeling is an essential early step towards managing landslide risk. A minimum of $4.8 million is lost due to landslide related damages every year in Illawara region of Australia. At present, Data mining and knowledge discovery techniques are becoming popular in building landslide susceptibility models due to their enhanced predictive performances. Until now, the lack of tools to undertake data extraction and making the predictions have limited the applicability of this novel technique in landslide model building. This paper discusses the development of the LSDM (Landslide Susceptibility Data Mining) toolbar which was designed to utilize machine learning techniques within a GIS environment by coupling GIS and data mining software (See5) capabilities. The software development kit available with ArcGIS v.10 has been utilized in developing the toolbar add-in. The fundamental tasks; data preparation, model optimizing, derivation of decision trees, predictions and validation are all performed using the individual controls available in the toolbar. This tool automates the entire model building process and in preparation of training data and producing outcomes that are compliant with both national and international Landslide Risk management guidelines.

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Recommendations for the quantitative analysis of landslide risk

Cited by 586 publications

References 277 publications

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Risk-based analysis of monitoring time intervals for landslide prevention

ArcGIS V.10 Landslide Susceptibility Data Mining Add-in Tool Integrating Data Mining and GIS Techniques to Model Landslide Susceptibility

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