@article{eprints1765,
          number = {S2},
           pages = {1--10},
           month = {December},
          volume = {62},
            year = {2013},
           title = {Flood hazard assessment via threshold binary classifiers: case study of the Tanaro River basin},
          author = {Massimiliano Degiorgis and Giorgio Gnecco and Silvia Gorni and Giorgio Roth and Marcello Sanguineti and Angela Celeste Taramasso},
       publisher = {Wiley-Blackwell},
         journal = {Irrigation and Drainage},
            note = {Article first published online: 3 DEC 2013},
        keywords = {flood risk detection; flood hazard management; area under the ROC curve; threshold binary classification; Shuttle radar topography mission},
        abstract = {This 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\%},
             url = {http://eprints.imtlucca.it/1765/}
}