scholarly journals INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis

2016 ◽  
Vol 16 (12) ◽  
pp. 2577-2591 ◽  
Author(s):  
Francesco Dottori ◽  
Rui Figueiredo ◽  
Mario L. V. Martina ◽  
Daniela Molinari ◽  
Anna Rita Scorzini
Keyword(s):  
Damage Assessment ◽  
Damage Model ◽  
Flood Damage ◽  
Physical Damage ◽  
Damage Functions ◽  
Technical Literature ◽  
Actual Knowledge ◽  
Damage Models ◽  
Model Based ◽  
Risk Assessment And Management

Abstract. Methodologies to estimate economic flood damages are increasingly important for flood risk assessment and management. In this work, we present a new synthetic flood damage model based on a component-by-component analysis of physical damage to buildings. The damage functions are designed using an expert-based approach with the support of existing scientific and technical literature, loss adjustment studies, and damage surveys carried out for past flood events in Italy. The model structure is designed to be transparent and flexible, and therefore it can be applied in different geographical contexts and adapted to the actual knowledge of hazard and vulnerability variables. The model has been tested in a recent flood event in northern Italy. Validation results provided good estimates of post-event damages, with similar or superior performances when compared with other damage models available in the literature. In addition, a local sensitivity analysis was performed in order to identify the hazard variables that have more influence on damage assessment results.

scholarly journals INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis

2016 ◽  
Author(s):  
Francesco Dottori ◽  
Rui Figueiredo ◽  
Mario Martina ◽  
Daniela Molinari ◽  
Anna Rita Scorzini
Keyword(s):  
Damage Assessment ◽  
Damage Model ◽  
Flood Damage ◽  
Physical Damage ◽  
Damage Functions ◽  
Technical Literature ◽  
Actual Knowledge ◽  
Damage Models ◽  
Model Based ◽  
Risk Assessment And Management

Abstract. Methodologies to estimate economic flood damages are increasingly important for flood risk assessment and management. In this work, we present a new synthetic flood damage model based on a component-by-component analysis of physical damage to buildings. The damage functions are designed using an expert-based approach with the support of existing scientific and technical literature, and have been calibrated with loss adjustment studies and damage surveys carried out for past flood events in Italy. The model structure is designed to be transparent and flexible, and therefore it can be applied in different geographical contexts and adapted to the actual knowledge of hazard and vulnerability variables. The model has been tested in a recent flood event in Northern Italy. Validation results provided good estimates of post-event damages, with better performances than most damage models available in the literature. In addition, a local sensitivity analysis has been performed, in order to identify the hazard variables that have more influence on damage assessment results.

scholarly journals Comparative flood damage model assessment: towards a European approach

2012 ◽  
Vol 12 (12) ◽  
pp. 3733-3752 ◽  
Author(s):  
B. Jongman ◽  
H. Kreibich ◽  
H. Apel ◽  
J. I. Barredo ◽  
P. D. Bates ◽  
...  
Keyword(s):  
Large Scale ◽  
Best Practice ◽  
Damage Model ◽  
Flood Damage ◽  
Model Assessment ◽  
Care Needs ◽  
Damage Functions ◽  
Damage Models ◽  
European Approach ◽  
Asset Values

Abstract. There is a wide variety of flood damage models in use internationally, differing substantially in their approaches and economic estimates. Since these models are being used more and more as a basis for investment and planning decisions on an increasingly large scale, there is a need to reduce the uncertainties involved and develop a harmonised European approach, in particular with respect to the EU Flood Risks Directive. In this paper we present a qualitative and quantitative assessment of seven flood damage models, using two case studies of past flood events in Germany and the United Kingdom. The qualitative analysis shows that modelling approaches vary strongly, and that current methodologies for estimating infrastructural damage are not as well developed as methodologies for the estimation of damage to buildings. The quantitative results show that the model outcomes are very sensitive to uncertainty in both vulnerability (i.e. depth–damage functions) and exposure (i.e. asset values), whereby the first has a larger effect than the latter. We conclude that care needs to be taken when using aggregated land use data for flood risk assessment, and that it is essential to adjust asset values to the regional economic situation and property characteristics. We call for the development of a flexible but consistent European framework that applies best practice from existing models while providing room for including necessary regional adjustments.

scholarly journals Multi-variate flood damage assessment: a tree-based data-mining approach

2013 ◽  
Vol 13 (1) ◽  
pp. 53-64 ◽  
Author(s):  
B. Merz ◽  
H. Kreibich ◽  
U. Lall
Keyword(s):  
Data Mining ◽  
Damage Assessment ◽  
Economic Status ◽  
Flood Damage ◽  
Damage Functions ◽  
Data Set ◽  
Promising Alternative ◽  
Damage Models ◽  
Inundation Depth ◽  
Flood Damage Assessment

Abstract. The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record contains details about a large variety of potential damage-influencing characteristics, such as hydrological and hydraulic aspects of the flooding situation, early warning and emergency measures undertaken, state of precaution of the household, building characteristics and socio-economic status of the household. Regression trees and bagging decision trees are used to select the more important damage-influencing variables and to derive multi-variate flood damage models. It is shown that these models outperform existing models, and that tree-structured models are a promising alternative to traditional damage models.

scholarly journals INSYDE-BE: Adaptation of the INSYDE model to the Walloon Region (Belgium)

2021 ◽  
Author(s):  
Anna Rita Scorzini ◽  
Benjamin Dewals ◽  
Daniela Rodriguez Castro ◽  
Pierre Archambeau ◽  
Daniela Molinari
Keyword(s):  
Flood Risk ◽  
Damage Model ◽  
Flood Damage ◽  
Original Model ◽  
Risk Assessments ◽  
Damage Functions ◽  
Residential Sector ◽  
Damage Models ◽  
Similarities And Differences ◽  
Walloon Region

Abstract. The spatial transfer of flood damage models among regions and countries is a challenging but unavoidable approach, for performing flood risk assessments in data and model scarce regions. In these cases, similarities and differences between the contexts of application should be considered to obtain reliable damage estimations and, in some cases, the adaptation of the original model to the new conditions is required. This study exemplifies a replicable procedure for the adaptation to the Belgian context of a multi-variable, synthetic flood damage model for the residential sector originally developed for Italy (INSYDE). The study illustrates necessary amendments in model assumptions, especially regarding input default values for the hazard and building parameters and damage functions describing the modelled damage mechanisms.

scholarly journals Ex post damage assessment: an Italian experience

2014 ◽  
Vol 14 (4) ◽  
pp. 901-916 ◽  
Author(s):  
D. Molinari ◽  
S. Menoni ◽  
G. T. Aronica ◽  
F. Ballio ◽  
N. Berni ◽  
...  
Keyword(s):  
Data Collection ◽  
Damage Assessment ◽  
Local Level ◽  
Data Gathering ◽  
Recovery Phase ◽  
Flood Damage ◽  
Local Authorities ◽  
Damage Models ◽  
Ex Post ◽  
And Storage

Abstract. In recent years, awareness of a need for more effective disaster data collection, storage, and sharing of analyses has developed in many parts of the world. In line with this advance, Italian local authorities have expressed the need for enhanced methods and procedures for post-event damage assessment in order to obtain data that can serve numerous purposes: to create a reliable and consistent database on the basis of which damage models can be defined or validated; and to supply a comprehensive scenario of flooding impacts according to which priorities can be identified during the emergency and recovery phase, and the compensation due to citizens from insurers or local authorities can be established. This paper studies this context, and describes ongoing activities in the Umbria and Sicily regions of Italy intended to identifying new tools and procedures for flood damage data surveys and storage in the aftermath of floods. In the first part of the paper, the current procedures for data gathering in Italy are analysed. The analysis shows that the available knowledge does not enable the definition or validation of damage curves, as information is poor, fragmented, and inconsistent. A new procedure for data collection and storage is therefore proposed. The entire analysis was carried out at a local level for the residential and commercial sectors only. The objective of the next steps for the research in the short term will be (i) to extend the procedure to other types of damage, and (ii) to make the procedure operational with the Italian Civil Protection system. The long-term aim is to develop specific depth–damage curves for Italian contexts.

scholarly journals Estimation of an absolute flood damage curve based on an Austrian case study under a dam breach scenario

2010 ◽  
Vol 10 (4) ◽  
pp. 881-894 ◽  
Author(s):  
F. Prettenthaler ◽  
P. Amrusch ◽  
C. Habsburg-Lothringen
Keyword(s):  
Future Development ◽  
Damage Assessment ◽  
Field Data ◽  
Flood Damage ◽  
Damage Function ◽  
Damage Potential ◽  
Dam Breach ◽  
Damage Functions ◽  
Austrian Case

Abstract. To date, in Austria no empirical assessment of absolute damage curves has been realized on the basis of detailed information on flooded buildings due to a dam breach, presumably because of the lack of data. This paper tries to fill this gap by estimating an absolute flood-damage curve, based on data of a recent flood event in Austria in 2006. First, a concise analysis of the case study area is conducted, i.e., the maximum damage potential is identified by using raster-based GIS. Thereafter, previous literature findings on existing flood-damage functions are considered in order to determine a volume-water damage function that can be used for further flood damage assessment. Finally, the flood damage function is cross validated and applied in prediction of damage potential in the study area. For future development of the estimated flood damage curve, and to aid more general use, we propose verification against field data on damage caused by natural waves in rivers.

scholarly journals Data-mining for multi-variable flood damage modelling with limited data

2017 ◽  
Author(s):  
Dennis Wagenaar ◽  
Jurjen de Jong ◽  
Laurens M. Bouwer
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Water Depth ◽  
Damage Assessment ◽  
Damage Model ◽  
Residential Building ◽  
Regression Trees ◽  
Flood Damage ◽  
Data Mining Techniques ◽  
Flood Damage Assessment

Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Recent studies have shown that data-mining techniques applied to a multi-dimensional dataset can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive dataset, which is rarely available and this can limit the application of these new techniques. In this paper we enrich a dataset of residential building and content damages from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2D flood simulations are used to add information on flow velocity, flood duration and the return period to the dataset, and cadastre data is used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched dataset in combination with the data-mining techniques delivers a significant improvement over a simple model only based on the water depth. We find that with our dataset, the trees based methods perform better than the Bayesian Network.

scholarly journals AGRIDE-c, a conceptual model for the estimation of flood damage to crops: development and implementation

2019 ◽  
Author(s):  
Daniela Molinari ◽  
Anna Rita Scorzini ◽  
Alice Gallazzi ◽  
Francesco Ballio
Keyword(s):  
Conceptual Model ◽  
Damage Assessment ◽  
Risk Mitigation ◽  
Cost Benefit ◽  
Flood Damage ◽  
Economic Consequences ◽  
Assessment Process ◽  
Physical Damage ◽  
Damage Mechanisms ◽  
Po Plain

Abstract. This paper presents AGRIDE-c, a conceptual model for the assessment of flood damage to crops. All available knowledge on damage mechanisms triggered by inundation phenomena is systematised in a usable and consistent tool, with the main strength represented by the integration of physical damage assessment with the evaluation of its economic consequences on farmers’ gross product. This allows AGRIDE-c to be used to guide the flood damage assessment process in different geographical and economic contexts, as demonstrated by the example provided in this study for the Po Plain (North of Italy). The development and implementation of the model highlighted that a thorough understanding and modelling of damage mechanisms to crops allows for comprehensive cost-benefit analyses of risk mitigation actions, and is a powerful tool to orient farmers’ behaviour towards more resilient damage alleviation practices.

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