Name
Abstract | ||
|---|---|---|
8 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. 9 |
Year | Venue | Field |
|---|---|---|
2016 | PeerJ PrePrints | Training set,Paleontology,Data mining,Population,Biology,Multivariate statistics,Terrain,Statistical algorithm,Statistical model,Elevation,Flood myth,Cartography |
DocType | Volume | Citations |
Journal | 4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivan Marchesini | 1 | 0 | 1.35 |
| Mauro Rossi | 2 | 91 | 15.55 |
| Paola Salvati | 3 | 2 | 1.05 |
| Marco Donnini | 4 | 0 | 0.68 |
| Simone Sterlacchini | 5 | 21 | 4.91 |
| Fausto Guzzetti | 6 | 19 | 3.35 |