Preprints
https://doi.org/10.5194/nhess-2021-253
https://doi.org/10.5194/nhess-2021-253

  15 Sep 2021

15 Sep 2021

Review status: this preprint is currently under review for the journal NHESS.

Machine-Learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains

Andrea Magnini1, Michele Lombardi2, Simone Persiano1, Antonio Tirri3, Francesco Lo Conti3, and Attilio Castellarin1 Andrea Magnini et al.
  • 1Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
  • 2Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy
  • 3Leithà, Unipol Group, Milan and Bologna, Italy

Abstract. Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e. FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas, and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through Decision Tree models trained on target FH maps, referring to a large study area (≈105 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (overall accuracy: 93 %) relative to univariate ones (overall accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ≈0.7), and (c) produce encouraging results in extrapolation.

Andrea Magnini et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-253', Caterina Samela, 14 Oct 2021 reply

Andrea Magnini et al.

Andrea Magnini et al.

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Short summary
We retrieve descriptors of the terrain morphology from a digital elevation model of a 105 km2 study area, and blend them through Decision Tree models to map flood susceptibility and expected water depth. We investigate this approach, with particular attention to (a) the comparison with a selected single-descriptor approach, (b) the goodness of Decision Trees, and (c) the performance of these models when applied to data-scarce regions. We find promising pathways for future research.
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