eprintid: 1753
rev_number: 8
eprint_status: archive
userid: 46
dir: disk0/00/00/17/53
datestamp: 2013-09-17 07:43:57
lastmod: 2013-09-17 07:43:57
status_changed: 2013-09-17 07:43:57
type: article
succeeds: 1727
metadata_visibility: show
creators_name: Degiorgis, Massimiliano
creators_name: Gnecco, Giorgio
creators_name: Gorni, Silvia
creators_name: Roth, Giorgio
creators_name: Sanguineti, Marcello
creators_name: Taramasso, Angela Celeste
creators_id: 
creators_id: giorgio.gnecco@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
creators_id: 
title: Classifiers for the Detection of Flood-Prone Areas Using Remote Sensed Elevation Data
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Flood hazard; Flood risk management; Receiver operating characteristics; Linear classifiers and support-vector machines; Parameter optimization; Shuttle radar topography mission
abstract: Summary A technique is presented for the identification of the areas subject to flooding hazard. Starting from remote sensed elevation data and existing flood hazard maps – usually available for limited areas – the relationships between selected quantitative morphologic features and the flooding hazard are first identified and then used to extend the hazard information to the entire catchment. This is performed through techniques of pattern classification, such as linear classifiers based on quantitative morphologic features, and support vector machines with linear and Gaussian kernels. The experiment starts by discriminating between flood-prone areas and marginal hazard areas. Multiclass classifiers are subsequently used to graduate the hazard. Their designs amount to solving suitable optimization problems. Several performance measures are considered in comparing the different classifiers, such as the area under the receiver operating characteristics curve, and the sum of the false positive and false negative rates. The procedure has been validated for the Tanaro basin, a tributary to the major Italian river, the Po. Results show a high reliability: the classifier properly identifies 93 of flood-prone areas, and only 14 of the areas subject to a marginal hazard are improperly assigned. An increase of this latter value up to 19 is detected when the same structure is applied for hazard graduation. Results derived from the application to different catchments seem to qualitatively indicate the ability of the classifier to perform well also outside the calibration region. Pattern classification techniques should be considered when the identification of flood-prone areas and hazard grading is required for large regions (e.g., for civil protection or insurance purposes) or when a first identification is needed (e.g., to address further detailed flood-mapping activities). 
date: 2012
date_type: published
publication: Journal of Hydrology
volume: 470-1
publisher: Elsevier
pagerange: 302-315
id_number: 10.1016/j.jhydrol.2012.09.006
refereed: TRUE
issn: 0022-1694
official_url: http://www.sciencedirect.com/science/article/pii/S002216941200755X
citation:   Degiorgis, Massimiliano and Gnecco, Giorgio and Gorni, Silvia and Roth, Giorgio and Sanguineti, Marcello and Taramasso, Angela Celeste  Classifiers for the Detection of Flood-Prone Areas Using Remote Sensed Elevation Data.  Journal of Hydrology, 470-1.  pp. 302-315.  ISSN 0022-1694      (2012)