Classifiers for the detection of flood-prone areas using remote sensed elevation data

SummaryA 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).

► We propose techniques to identify flood prone areas from digital elevation models.
► Adopted techniques are based on pattern classification and geomorphologic features.
► Available flood hazard information is extended to cover the entire catchment.
► Several performance measures are considered in comparing the different classifiers.
► The classifier properly identifies 93% of the case study flood prone areas.