http://www.cnr.it/ontology/cnr/individuo/prodotto/ID180940
Artificial neural networks and cluster analysis in landslide susceptibility zonation (Articolo in rivista)
- Type
- Label
- Artificial neural networks and cluster analysis in landslide susceptibility zonation (Articolo in rivista) (literal)
- Anno
- 2008-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.geomorph.2006.10.035 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Melchiorre C.; Matteucci M.; Azzoni A.; Zanchi A. (literal)
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- Rivista
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- Note
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- C. 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 (literal)
- Titolo
- Artificial neural networks and cluster analysis in landslide susceptibility zonation (literal)
- Abstract
- A 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. (literal)
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