Recommendations for the quantitative analysis of landslide risk
Jordi Corominas,C.J. van Westen,Paolo Frattini,Leonardo Cascini,Jean-Philippe Malet,Stavroula Fotopoulou,Filippo Catani,M. van den Eeckhaut,Olga Mavrouli,Federico Agliardi,Kyriazis Pitilakis,Mike G. Winter,Manuel Pastor,Settimio Ferlisi,Veronica Tofani,Javier Hervás,J.T. Smith +16 more
TLDR
In this article, the authors present recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results.Abstract:
This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.read more
Citations
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Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts
Xuanmei Fan,Gianvito Scaringi,Gianvito Scaringi,Oliver Korup,A. Joshua West,Cees J. van Westen,Hakan Tanyas,Niels Hovius,Tristram Hales,Randall W. Jibson,Kate E. Allstadt,Li Min Zhang,Stephen G. Evans,Chong Xu,Gen Li,Xiangjun Pei,Qiang Xu,Runqiu Huang +17 more
TL;DR: In this paper, the authors analyze how earthquakes trigger landslides and highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface, highlighting research gaps.
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Review on landslide susceptibility mapping using support vector machines
TL;DR: A review of landslide susceptibility mapping using SVM, a machine learning algorithm that uses a small number of samples for prediction and has been widely used in recent years, and its strengths and weaknesses.
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Prediction of the landslide susceptibility: Which algorithm, which precision?
TL;DR: The first comprehensive comparison among the performances of ten advanced machine learning techniques (MLTs) including artificial neural networks (ANNs), boosted regression tree (BRT), classification and regression trees (CART), generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS), naive Bayes (NB), quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM) is presented.
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Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)
TL;DR: In this paper, the authors defined reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques and evaluated the predictive capabilities of the models using ROC curve, AUC and contingency tables.
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