Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA review of statistically-based landslide susceptibility models
Anno di pubblicazione2018
Autore/iPaola Reichenbach (1), Mauro Rossi (1), Bruce Malamud (2), Monika Mihir (2,3), and Fausto Guzzetti (1)
Affiliazioni autori(1) CNR IRPI, via Madonna Alta 126, I-06128 Perugia, Italy (2) Department of Geography, King's College London, Strand, London, UK (3) Consultant at Mapmechanics, Canal Court, 155 High Street, Brentford, UK
Autori CNR e affiliazioni
  • inglese medio (1100-1500)
AbstractIn this paper, we do a critical review of statistical methods for landslide susceptibility assessment and associated terrain zonations. Landslide susceptibility is the likelihood of a landslide occurring in an area depending on local terrain conditions, estimating "where" landslides are likely to occur. Since the first attempts to assess landslide susceptibility in the mid-1970s, hundreds of papers have been published using a variety of approaches and methods in different geological and climatic settings. Here, we critically review the statistically-based landslide susceptibility assessment literature by systematically searching for and then compiling an extensive database of 565 peer-review articles from 1983 to 2016. For each article in the literature database, we noted 31 categories/sub-categories of information including study region/extent, landslide type/number, inventory type and period covered, statistical model used, including variable types, model fit/prediction performance evaluation method, and strategy used to assess the model uncertainty. We present graphical visualisations and discussions of commonalities and differences found as a function of region and time, revealing a significant heterogeneity of thematic data types and scales, modelling approaches, and model evaluation criteria. We found that the range of thematic data types used for susceptibility assessment has not changed significantly with time, and that for a number of studies the geomorphological significance of the thematic data used is poorly justified. We also found that the most common statistical methods for landslide susceptibility assessment include logistic regression, neural network analysis, data-overlay, index-based and weight of evidence analyses, with an increasing preference towards machine learning methods in the recent years. Although an increasing number of studies in recent years have assessed the model performance, in terms of model fit and prediction performance, only a handful of studies have evaluated the model uncertainty. Adopting a Susceptibility Quality Level index, we found that the quality of published models has improved over the years, but top-quality assessments remain rare. We identified a clear geographical bias in susceptibility study locations, with many studies in China, India, Italy, South Korea and Turkey, and only a few in Africa, South America and Oceania. Based on previous literature reviews, the analysis of the information collected in the literature database, and our own experience on the subject, we provide recommendations for the preparation, evaluation, and use of landslide susceptibility models and associated terrain zonations.
Lingua abstractinglese
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RivistaEarth-science reviews
Attiva dal 1966
Editore: Elsevier - Oxford ;
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0012-8252
Titolo chiave: Earth-science reviews
Titolo proprio: Earth-science reviews.
Titolo abbreviato: Earth-sci. rev.
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Verificato da refereeSì: Internazionale
Stato della pubblicazionePreprint
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85044461142)
  • ISI Web of Science (WOS) (Codice:000433645900004)
Parole chiaveliterature review; landslide; susceptibility zonation; statistical model.
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Strutture CNR
  • IRPI — Istituto di ricerca per la protezione idrogeologica
Moduli/Attività/Sottoprogetti CNR
  • DTA.AD003.150.001 : Mappatura, Modellazione e previsione Multiscala di RIschi GEo-idroLogici
Progetti Europei
A review of statistically-based landslide susceptibility models (documento privato )
Tipo documento: application/pdf