Optimal landslide susceptibility zonation based on multiple forecasts

Rossi, Mauro; Guzzetti, Fausto; Reichenbach, Paola; Mondini, Alessandro Cesare; Peruccacci, Silvia

doi:10.1016/j.geomorph.2009.06.020

Cited by 323 publications

(258 citation statements)

References 56 publications

(120 reference statements)

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“…Süzen and Doyuran, 2004;Brenning, 2005;van den Eeckhaut et al, 2009;Rossi et al, 2010;Nandi and Shakoor, 2010;Oh et al, 2010;Pradhan and Lee, 2010) and has already given good results in similar hilly environments (van den Eeckhaut et al, 2009;van den Eeckhaut et al, 2010). The method can be directly implemented into GIS software (Kemp et al, 2001;Sawatzky et al, 2009a, b).…”

Section: Fressard Et Al: Which Data For Quantitative Landslide Sumentioning

confidence: 99%

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Fressard

Thiéry²,

Maquaire

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper aims at assessing the impact of the data set quality for landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted on the Pays d'Auge plateau (Normandy, France) with a scale objective of 1 / 10 000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, and geomorphological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlight that only high-quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formation maps) can predict a satisfying proportion of landslides in the study area.

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Section: Fressard Et Al: Which Data For Quantitative Landslide Sumentioning

confidence: 99%

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Fressard

Thiéry²,

Maquaire

2014

Nat. Hazards Earth Syst. Sci.

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show abstract

“…One of the main difficulties resides in determining how to combine the models for application to a specific area. In landslide susceptibility assessment this can be considered as an unresolved problem [46][47][48] in [41]. In other disciplines, for example meteorology, a long experience in combining multiple model forecasts already exist [49].…”

Section: Resultsmentioning

confidence: 99%

“…In other disciplines, for example meteorology, a long experience in combining multiple model forecasts already exist [49]. Exploiting ensemble analysis to obtain the most probable forecast is a common practice [41]. …”

Section: Resultsmentioning

confidence: 99%

“…The landslide susceptibility maps calculated applying the statistical methods were obtained using three different scripts [60] implemented using MATLAB and R languages respectively in GNU Octave and GNU R free software along with the modelling library Mastrave which implements the semantic array programming (SemAP) paradigm. The script applied for calculating the ANN is based on the work of Rossi [41]. Also the scripts used for selecting the training and testing points and for calculating the combined model [60] were written following the SemAP paradigm in MATLAB language, within GNU Octave as computing environment.…”

Section: Resultsmentioning

confidence: 99%

“…For running the statistical models a set of calibration parameters was selected from international literature [37,39,41]. Seven parameters, commonly assumed as directly or indirectly related to landslide occurrence [37,39], have been used for calculating the ANN, LR and RDS models.…”

Section: Data and Explanatory Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Multi-scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble

Bosco

Rigo

Dijkstra

et al. 2013

IFIP Advances in Information and Communication Technology

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Abstract. Landslide susceptibility assessment is a fundamental component of effective landslide prevention. One of the main challenges in landslides forecasting is the assessment of spatial distribution of landslide susceptibility. Despite the many different approaches, landslide susceptibility assessment still remains a challenge. A semi-quantitative method is proposed combining heuristic, deterministic and probabilistic approaches for a robust catchment scale assessment. A fuzzy ensemble model has been exploited for aggregating an array of different susceptibility zonation maps. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN) and two different landslide susceptibility techniques based on the infinite slope stability model. The sequence of data-transformation models has been enhanced following the semantic array programming paradigm. The ensemble has been applied to a study area in Italy. This catchment scale methodology may be exploited for analysing the potential impact of landscape disturbances. At regional scale, a qualitative approach is also proposed as a rapid assessment technique -suitable for application in real-time operations such as wildfire emergency management.

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Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

Manconi

Picozzi

Coviello

et al. 2016

Geophysical Research Letters

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We propose a new real‐time approach to detect, locate, and estimate the volume of rockslides by analyzing waveforms acquired from broadband regional seismic networks. The identification of signals generated by rockslides from other sources, such as natural and/or induced earthquakes, is accomplished by exploiting the ratio between local magnitudes (ML) and duration magnitudes (MD). We found that signals associated with rockslides have ML/MD < 0.8, while for earthquakes ML/MD ≅ 1. In addition, we derived an empirical relationship between MD and rockslide volumes, obtaining a preliminary characterization of rockslide volume within seconds after their occurrence. The key points of this study are presented by testing the hypothesis on a recent rockslide event that occurred in northern Italy. We discuss also the potential evolution of the methodology for early warning and/or rapid response purposes.

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Optimal landslide susceptibility zonation based on multiple forecasts

Cited by 323 publications

References 56 publications

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

Multi-scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble

Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

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