IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T08:08:45ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-02-26T15:00:45Z2016-02-26T15:00:45Zhttp://eprints.imtlucca.it/id/eprint/3132This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/31322016-02-26T15:00:45ZEvaluating flood hazard at the catchment scale via machine-learning techniquesMassimiliano DegiorgisGiorgio Gneccogiorgio.gnecco@imtlucca.itSilvia GorniRita Morisirita.morisi@imtlucca.itGiorgio RothMarcello SanguinetiAngela Celeste Taramasso2015-10-19T10:16:18Z2016-02-26T11:47:36Zhttp://eprints.imtlucca.it/id/eprint/2778This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/27782015-10-19T10:16:18ZSupervised and semi-supervised classifiers for the detection of flood-prone areasSupervised and semi-supervised machine-learning techniques are applied and compared for the recognition of the flood hazard. The learning goal consists in distinguishing between flood-exposed and marginal-risk areas. Kernel-based binary classifiers using six quantitative morphological features, derived from data stored in digital elevation models, are trained to model the relationship between morphology and the flood hazard. According to the experimental outcomes, such classifiers are appropriate tools when one is interested in performing an initial low-cost detection of flood-exposed areas, to be possibly refined in successive steps by more time-consuming and costly investigations by experts. The use of these automatic classification techniques is valuable, e.g., in insurance applications, where one is interested in estimating the flood hazard of areas for which limited labeled information is available. The proposed machine-learning techniques are applied to the basin of the Italian Tanaro River. The experimental results show that for this case study, semi-supervised methods outperform supervised ones when—the number of labeled examples being the same for the two cases—only a few labeled examples are used, together with a much larger number of unsupervised ones.Giorgio Gneccogiorgio.gnecco@imtlucca.itRita Morisirita.morisi@imtlucca.itGiorgio RothMarcello SanguinetiAngela Celeste Taramasso2013-09-17T12:59:48Z2014-01-29T10:28:09Zhttp://eprints.imtlucca.it/id/eprint/1765This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/17652013-09-17T12:59:48ZFlood hazard assessment via threshold binary classifiers: case study of the Tanaro River basinThis contribution deals with the identification of flood hazards at the catchment scale. The aim is to distinguish flood-exposed areas from marginal risk ones, and to extend available information on flood hazards to cover the whole catchment. Threshold binary classifiers based on six selected quantitative morphological features, derived from data stored in digital elevation models (DEMs), are used to investigate the relationships between morphology and the flooding hazard, as described in flood hazard maps. Results show that threshold binary classifier techniques should be taken into account when one is interested in an initial low-cost detection of flood-exposed areas. This may be needed, for example, in applications related to the insurance market, in which one is interested in estimating the flood hazard of specific areas for which limited information is available, or whenever a first flood hazard delineation is required to further address detailed investigations for flood mapping purposes. The method described in the paper has been tested on the basin of the Tanaro River. Results present a high degree of accuracy: indeed, the best classifier correctly identifies about 91% of flood-exposed areas, whereas the percentage of the areas exposed to marginal risk that are incorrectly classified as flood-exposed areas is about 16%Massimiliano DegiorgisGiorgio Gneccogiorgio.gnecco@imtlucca.itSilvia GorniGiorgio RothMarcello SanguinetiAngela Celeste Taramasso2013-09-17T07:56:24Z2013-09-17T07:56:24Zhttp://eprints.imtlucca.it/id/eprint/1752This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/17522013-09-17T07:56:24ZClassifiers for the Detection of Flood Prone Areas from Remote Sensed Elevation DataMassimiliano DegiorgisGiorgio Gneccogiorgio.gnecco@imtlucca.itSilvia GorniGiorgio RothMarcello SanguinetiAngela Celeste Taramasso2013-09-17T07:43:57Z2013-09-17T07:43:57Zhttp://eprints.imtlucca.it/id/eprint/1753This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/17532013-09-17T07:43:57ZClassifiers for the Detection of Flood-Prone Areas Using Remote Sensed Elevation DataSummary 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). Massimiliano DegiorgisGiorgio Gneccogiorgio.gnecco@imtlucca.itSilvia GorniGiorgio RothMarcello SanguinetiAngela Celeste Taramasso