Delineating flood prone areas using a statistical approach
Author and article information
Abstract
Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.
Cite this as
2016. Delineating flood prone areas using a statistical approach. PeerJ Preprints 4:e1937v2 https://doi.org/10.7287/peerj.preprints.1937v2Author comment
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Sections
Supplemental Information
Boundaries of the Italian River Basin Authorities
Boundaries of the Italian River Basin Authorities (RBAs). Red shows catchments considered in this study.
Logical framework and algorithm
Logical framework and algorithm adopted for the production of Logistic Regression classification models for flood hazard zonation, and the preparation of the modeled, statistically based binary FHMs for selected areas in Italy.
Comparison of FHMS
Comparison of FHMS produced by River Basin Authorities and prepared in this study using a statistically-based zonation
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Ivan Marchesini conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work.
Mauro Rossi conceived and designed the experiments, contributed reagents/materials/analysis tools, performed the computation work, reviewed drafts of the paper.
Paola Salvati conceived and designed the experiments, reviewed drafts of the paper.
Marco Donnini conceived and designed the experiments, reviewed drafts of the paper, performed a test analysis with the first author.
Simone Sterlacchini conceived and designed the experiments, reviewed drafts of the paper.
Fausto Guzzetti conceived and designed the experiments, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
Data is available online at different web resources:
- FHMs: http://mappa.italiasicura.gov.it/#/opendata
- EU-DEM: http://www.eea.europa.eu/data-and-maps/data/eu-dem
- TINITALY: http://tinitaly.pi.ingv.it/
Funding
Work partially supported by a grant of the Fondazione Assicurazioni Generali, Trieste. M. Donnini was partially supported by this grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
