Articles | Volume 19, issue 3
Nat. Hazards Earth Syst. Sci., 19, 661–678, 2019
https://doi.org/10.5194/nhess-19-661-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: Flood risk assessment and management
Nat. Hazards Earth Syst. Sci., 19, 661–678, 2019
https://doi.org/10.5194/nhess-19-661-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 29 Mar 2019
Research article | 29 Mar 2019
Testing empirical and synthetic flood damage models: the case of Italy
Mattia Amadio et al.
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Cited
25 citations as recorded by crossref.
- Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
- Flood risk assessment of cultural heritage at large spatial scales: Framework and application to mainland Portugal R. Figueiredo et al. 10.1016/j.culher.2019.11.007
- Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
- Evaluating targeted heuristics for vulnerability assessment in flood impact model chains A. Zischg et al. 10.1111/jfr3.12736
- Flood vulnerability and risk assessment of urban traditional buildings in a heritage district of Kuala Lumpur, Malaysia D. D'Ayala et al. 10.5194/nhess-20-2221-2020
- Residential flood loss estimated from Bayesian multilevel models G. Mohor et al. 10.5194/nhess-21-1599-2021
- A model taxonomy for flood fragility and vulnerability assessment of buildings C. Galasso et al. 10.1016/j.ijdrr.2020.101985
- Comparison of two modelling strategies for 2D large-scale flood simulations S. Dazzi et al. 10.1016/j.envsoft.2021.105225
- Empirical flash flood vulnerability functions for residential buildings C. Arrighi et al. 10.1007/s42452-020-2696-1
- Preface: Advances in flood risk assessment and management C. Prieto et al. 10.5194/nhess-20-1045-2020
- Bayesian Data-Driven approach enhances synthetic flood loss models N. Sairam et al. 10.1016/j.envsoft.2020.104798
- Flood depth-damage and fragility functions derived with structured expert judgment G. Pita et al. 10.1016/j.jhydrol.2021.126982
- Multi-scenario flash flood hazard assessment based on rainfall–runoff modeling and flood inundation modeling: a case study Y. Zhang et al. 10.1007/s11069-020-04345-6
- Brief Communication: Simple-INSYDE, development of a new tool for flood damage evaluation from an existing synthetic model M. Galliani et al. 10.5194/nhess-20-2937-2020
- Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe D. Paprotny et al. 10.1016/j.scitotenv.2020.140011
- Flood risk assessment of the European road network K. van Ginkel et al. 10.5194/nhess-21-1011-2021
- Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
- Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. 10.1016/j.scitotenv.2021.145649
- The middle Huaihe River stability analysis and optimization of hydrological chaos forecasting model Y. Duan et al. 10.1080/19475705.2020.1815870
- A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
- Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
- The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. 10.1016/j.jhydrol.2020.125797
- Exploring machine learning potential for climate change risk assessment F. Zennaro et al. 10.1016/j.earscirev.2021.103752
- Flood Depth‒Damage Curves for Spanish Urban Areas E. Martínez-Gomariz et al. 10.3390/su12072666
- XGBoost-based method for flash flood risk assessment M. Ma et al. 10.1016/j.jhydrol.2021.126382
25 citations as recorded by crossref.
- Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
- Flood risk assessment of cultural heritage at large spatial scales: Framework and application to mainland Portugal R. Figueiredo et al. 10.1016/j.culher.2019.11.007
- Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
- Evaluating targeted heuristics for vulnerability assessment in flood impact model chains A. Zischg et al. 10.1111/jfr3.12736
- Flood vulnerability and risk assessment of urban traditional buildings in a heritage district of Kuala Lumpur, Malaysia D. D'Ayala et al. 10.5194/nhess-20-2221-2020
- Residential flood loss estimated from Bayesian multilevel models G. Mohor et al. 10.5194/nhess-21-1599-2021
- A model taxonomy for flood fragility and vulnerability assessment of buildings C. Galasso et al. 10.1016/j.ijdrr.2020.101985
- Comparison of two modelling strategies for 2D large-scale flood simulations S. Dazzi et al. 10.1016/j.envsoft.2021.105225
- Empirical flash flood vulnerability functions for residential buildings C. Arrighi et al. 10.1007/s42452-020-2696-1
- Preface: Advances in flood risk assessment and management C. Prieto et al. 10.5194/nhess-20-1045-2020
- Bayesian Data-Driven approach enhances synthetic flood loss models N. Sairam et al. 10.1016/j.envsoft.2020.104798
- Flood depth-damage and fragility functions derived with structured expert judgment G. Pita et al. 10.1016/j.jhydrol.2021.126982
- Multi-scenario flash flood hazard assessment based on rainfall–runoff modeling and flood inundation modeling: a case study Y. Zhang et al. 10.1007/s11069-020-04345-6
- Brief Communication: Simple-INSYDE, development of a new tool for flood damage evaluation from an existing synthetic model M. Galliani et al. 10.5194/nhess-20-2937-2020
- Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe D. Paprotny et al. 10.1016/j.scitotenv.2020.140011
- Flood risk assessment of the European road network K. van Ginkel et al. 10.5194/nhess-21-1011-2021
- Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
- Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. 10.1016/j.scitotenv.2021.145649
- The middle Huaihe River stability analysis and optimization of hydrological chaos forecasting model Y. Duan et al. 10.1080/19475705.2020.1815870
- A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
- Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
- The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. 10.1016/j.jhydrol.2020.125797
- Exploring machine learning potential for climate change risk assessment F. Zennaro et al. 10.1016/j.earscirev.2021.103752
- Flood Depth‒Damage Curves for Spanish Urban Areas E. Martínez-Gomariz et al. 10.3390/su12072666
- XGBoost-based method for flash flood risk assessment M. Ma et al. 10.1016/j.jhydrol.2021.126382
Discussed (final revised paper)
Latest update: 05 Dec 2021
Special issue
Short summary
Flood risk management relies on assessments performed using flood loss models of different complexities. We compared the performances of expert-based and empirical damage models on three major flood events in northern Italy. Our findings suggest that multivariate models have better potential to provide reliable damage estimates if extensive ancillary characterisation data are available. Expert-based approaches are better suited for transferability compared to empirically based approaches.
Flood risk management relies on assessments performed using flood loss models of different...
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