Supervised and semi-supervised classifiers for the detection of flood-prone areas

Supervised 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.