Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results (Articolo in rivista) (literal)

Anno

• 2015-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.5194/nhess-15-75-2015 (literal)

Alternative label

• Capecchi, V. and Perna, M. and Crisci, A. (2015)
  Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results
  in Natural hazards and earth system sciences (Print)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Capecchi, V. and Perna, M. and Crisci, A. (literal)

Pagina inizio

• 75 (literal)

Pagina fine

• 95 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.nat-hazards-earth-syst-sci.net/15/75/2015/ (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 15 (literal)

Rivista

• Natural hazards and earth system sciences (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 1 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, Via Madonna del piano 10, Sesto Fiorentino, 50019 Florence, Italy Consorzio LaMMA, Via Madonna del piano 10, Sesto Fiorentino, 50019 Florence, Italy Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, Via Caproni 8, 50145, Florence, Italy (literal)

Titolo

• Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results (literal)

Abstract

• We present a quantitative indirect statistical modeling for predicting rainfall-induced shallow landsliding. We consider as input layers both static thematic predictors, such as geomorphological, geological, climatological information, and numerical weather model's forecast. Two different statistical techniques are used to combine together the above mentioned predictors: a Generalized Linear Model and Breiman's Random Forests. We tested these two techniques for two rainfall events that occurred in 2011 and 2013 in Tuscany region (central Italy). Model's evaluation is measured by means of sensitivity-specificity ROC analysis. In the 2011 rainfall event, the Random Forests technique performs slightly better, whereas in the 2013 rainfall event the Generalized Linear Model provides more accurate predictions. This study seeks also to establish whether the rainfall-induced shallow landsliding prediction might substantially benefit from the information provided by the numerical weather model's outputs. Using the variable importance parameter provided by the Random Forests algorithm, we asses the added value carried by numerical weather forecast, in particular in the rainfall event characterized by deep atmospheric convection and heavy precipitations. (literal)

Prodotto di

• Institute for biometeorology (IBIMET) (Istituto)

Autore CNR

• VALERIO CAPECCHI (Unità di personale interno)
• MASSIMO PERNA (Persona)
• ALFONSO CRISCI (Unità di personale interno)

Incoming links:

Prodotto

• Institute for biometeorology (IBIMET) (Istituto)

Autore CNR di

• ALFONSO CRISCI (Unità di personale interno)
• MASSIMO PERNA (Persona)
• VALERIO CAPECCHI (Unità di personale interno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Natural hazards and earth system sciences (Rivista)