Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloArtificial neural networks and cluster analysis in landslide susceptibility zonation
Anno di pubblicazione2008
FormatoElettronico
Autore/iMelchiorre C.; Matteucci M.; Azzoni A.; Zanchi A.
Affiliazioni autoriC. Melchiorre, A. Zanchi D.I.S.A.T., Università degli Studi di Milano-Bicocca, 20126 Milan, Italy M. Matteucci D.E.I., Politecnico di Milano, 20133 Milan, Italy A. Azzoni Geologist, Via Nullo, 24100 Bergamo, Italy
Autori CNR e affiliazioni
  • ANDREA MARCO ZANCHI
Lingua/e
  • inglese
AbstractA landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da379
Pagine a400
Pagine totali22
RivistaGeomorphology (Amst.)
Attiva dal 1987
Editore: Elsevier - Oxford ;
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0169-555X
Titolo chiave: Geomorphology (Amst.)
Titolo proprio: Geomorphology. (Amst.)
Titolo abbreviato: Geomorphology (Amst.)
Numero volume della rivista94
Fascicolo della rivista-
DOI10.1016/j.geomorph.2006.10.035
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000253302700009)
  • Scopus (Codice:2-s2.0-38149109867)
Parole chiaveSusceptibility analysis, Landslides, Cluster analysis, Artificial neural networks, Lombardy Southern Alps
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IDPA — IDPA - Sede secondaria di Milano
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Artificial neural networks and cluster analysis in landslide susceptibility zonation (documento privato )
Tipo documento: application/pdf

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Editore
  • Elsevier B.V., Amsterdam (Belgio)