Consiglio Nazionale delle Ricerche

Tipo di prodottoContributo in atti di convegno
TitoloDelineating flood prone areas using a statistical approach
Anno di pubblicazione2018
FormatoElettronico
Autore/iI. Marchesini M. Rossi P. Salvati M. Donnini S. Sterlacchini F. Guzzetti
Affiliazioni autoriCNR IRPI, Perugia, Italy CNR IRPI, Perugia, Italy CNR IRPI, Perugia, Italy CNR IRPI, Perugia, Italy CNR, IDPA, Milano, Italy CNR IRPI, Perugia, Italy
Autori CNR e affiliazioni
  • SIMONE STERLACCHINI
  • MAURO ROSSI
  • IVAN MARCHESINI
  • PAOLA SALVATI
  • MARCO DONNINI
  • FAUSTO GUZZETTI
Lingua/e
  • inglese
AbstractFloods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geoenvironmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da207
Pagine a2012
Pagine totali6
Rivista-
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Numero volume della serie/collana-
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ISBN978 88 8080 283 9
DOI10.30437/ogrs2016_paper_28
Editore-
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveFlood, DEM, Hazard, Statistical model, Zonation, Machine Learning Algorithm
Link (URL, URI)http://www.irpi.cnr.it/conference-files/ogrs2016/28.pdf
Titolo convegno/congressoIV Open Source Geospatial Research and Education Symposium (OGRS2016)
Luogo convegno/congressoPerugia
Data/e convegno/congresso12/10/2016-14/10/2016
RilevanzaInternazionale
RelazioneContributo
Titolo parallelo-
Licenza-
Scadenza embargo-
Note/Altre informazioni-
Strutture CNR
  • IRPI — Istituto di ricerca per la protezione idrogeologica
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
flood_prone_areas
Tipo documento: application/pdf