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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Nov 2018

Research article | 20 Nov 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Estimating flood damage in Italy: empirical vs expert-based modelling approach

Mattia Amadio1, Anna Rita Scorzini2, Francesca Carisi3, Arthur H. Essenfelder1, Alessio Domeneghetti3, Jaroslav Mysiak1, and Attilio Castellarin3 Mattia Amadio et al.
  • 1CMCC Foundation – Euro-Mediterranean Center on Climate Change and Ca' Foscari University of Venice, Italy
  • 2Department of Civil, Environmental and Architectural Engineering, University of L'Aquila, L'Aquila, Italy
  • 3DICAM, Water Resources, University of Bologna, Italy

Abstract. Flood risk management generally relies on economic assessments performed using flood loss models of different complexity, ranging from simple univariable to more complex multivariable models. These latter accounts for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. In this paper we collected a comprehensive dataset related to three recent major flood events in Northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), buildings characteristics (size, type, quality, economic value) as well as reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performance of four literature flood damage models of different nature and complexity are compared with the performance of univariable, bivariable and multivariable models empirically developed for Italy and tested at the micro scale based upon observed records. The uni- and bivariable models are produced testing linear, logarithmic and square root regression while multivariable models are based on two machine learning techniques, namely Random Forest and Artificial Neural Networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management.

Mattia Amadio et al.
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Mattia Amadio et al.
Mattia Amadio et al.
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Publications Copernicus
Short summary
In order to reduce the economic impact of flood events in populated aeras we need to learn from past disasters. The research focuses on the evaluation of flood assessment methods of different complexity, testing them on empirical records from three major flood events in N Italy. A machine learning approach is put in practice to understand the relationship between risk predictors. The results highlight that expert-based impact models perform well already when a limited number of variables are used.
In order to reduce the economic impact of flood events in populated aeras we need to learn from...