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![Representation of all Italian regions and focus on those involved in the European project [30], with the historically flooded areas.](publication/355407342/figure/fig1/AS:11431281099460316@1669267885041/Representation-of-all-Italian-regions-and-focus-on-those-involved-in-the-European-project.png)
Representation of all Italian regions and focus on those involved in the European project [30], with the historically flooded areas.
Source publication
Quantitative data on observed flood ground effects are precious information to assess current risk levels and to improve our capability to forecast future flood damage, with the final aim of defining effective prevention policies and checking their success. This paper presents the first collection and analysis of flood damage claims produced in Ita...
Context in source publication
Context 1
... territory involved in the analysis has an area of 83,735 km 2 (28% of the whole country) with a population of 17,300,863 inhabitants (30% of the Italian total). As reported in Figure 3, flooded areas have been mapped all over this territory as the mashup of information coming from different sources (Fondazione Politecnico di Milano, Basin Authorities, Copernicus Emergency management service [28]), and they cover around 1230 km 2 (5.6% of the total plain surface of southern Italy). The dataset has been cleaned of those data that appeared not statistically significant, such as those from municipalities with just one or two claims and obvious typos and transcription errors: we are speaking of data in paper forms, consisting of many dozen fields and complicated to fill in without the assistance of a professional. ...
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Citations
... In the conventional approach, empirical and synthetic data were sourced through surveys, census, insurance claims payouts, and interviews. In later periods, GIS, satellite, and remote sensing techniques provided a more convenient solution as sources of data for building and flood-related information which improved time and cost efficiency and reduced the effort spent on field surveys [27,28]. Moreover, these data sources are typically applied in conjunction with traditional data sources, thus providing better solutions to the challenges of limited data availability [12]. ...
... Flood risk assessment, flood hazard mapping, and flood inundation modeling are a few instances of the spatial analysis attributes of GIS [94]. GIS has been used alongside satellite or aerial images and remote sensing data collected after flood events to obtain elevation models and create vulnerability exposure attributes [10,27,95,96]. Although remote sensing is incapable of quantifying damage on its own, it has proven to be efficient when combined with other techniques and tools, such as machine learning [84]. ...
Flood damage assessment (FDA) is an essential tool for evaluating flood damage, vulnerability, and risk to civil systems such as residential buildings. The outcome of an FDA depends on the spatial limits of the study and the complexity of the data. For microscale FDA, a high level of detail is required to assess flood damage. This study reviewed the existing methodologies in microscale FDA based on empirical and synthetic data selection methods for model development. The merits and challenges of these approaches are discussed. This review also proposes an integrated step for assessing the stages of FDA. This study contributes to the literature by providing insights into the methodologies adopted, particularly on a microscale basis, which has not been comprehensively discussed in the previous reviews. The findings of this study reveal that univariate modeling of flood damage is nevertheless popular among researchers. New advanced approaches, such as advanced machine learning and 3D models, are yet to gain prominence when compared with the univariate modeling that has recorded a high success. This review concludes that there is a need to adopt a combined empirical–synthetic approach in the selection of data for developing damage models. Further research is required in the areas of multivariate modeling (advanced machine learning), 3D BIM-GIS modeling, 3D visualization of damages, and projection of probabilities in flood damage predictions to buildings. These are essential for performance flood-based building designs and for promoting building resilience to flood damage.
... Indeed, people Moreover, intense activities are carried out over the flooded territories to map the flood's perimeter and take notes of the damages reported by citizens. Indeed, people affected by the flood can ask for economic compensation damages to private houses, public goods, and economic activities by filling in forms collected in the aftermath of the flood, which are then processed and stored by the region [22]. ...
... Damage claims are available for 406 municipalities that were strongly damaged by some past flood events [22]. Of these municipalities, around 60% show an average altitude of less than 350 m a.s.l. ...
Flood damage assessment is an essential asset in flood risk management, and the literature brings several models, most based on damage curves. This kind of approach is essential and well established but not suitable for being applied in the immediate aftermaths of a flood. Herein, a new application of a zero-order model for the Po-Venetian plain area, which covers more than 20% of the Italian territory, is conceived and implemented. At first, a calibration of the model with damage data coming from past events in the region is carried out, showing that the calibration is site-specific. In particular, it is shown how the exponent in the power-law form of the model is the same as obtained in a previous work on another territory, while the intercept, defined as specific damage, varies. Then the algorithm is applied to the area affected during the flood that hit the Emilia-Romagna region (Northern Italy), in December 2020, for validation. Knowledge of the affected area comes from the quick assessment procedure commonly performed by the National Fire Corps in any significant flood, employing low-altitude all-weather helicopter GPS surveys. The paper shows that combining the model with such a rapid mapping technique leads to a near real-time estimation of damages in the aftermath of floods. The results are aimed at obtaining a first approximation figure. The estimation error shows this figure to be not far from those obtained with much more detailed approaches, requiring a much longer time for their setting. For that reason, the model results can contribute, among other things, by directing the immediate post-emergency management operations and promptly allocating financial resources for recovery.
The evaluation of flood impacts to economic activities is characterized by obstacles like the paucity of available data to characterise the enterprises, the lack of high-quality data to derive new models or validate existing ones, and the high variability of activity types which hampers generalisation. This study contributes at improving knowledge about types and extent of impacts of flood events on economic activities through the analysis of empirical data, focusing on direct damage and with specific reference to the Italian context. The collaboration among different research groups allowed to collect around a thousand of observed damage records after four flood events in Italy: the inundations that occurred in the town of Lodi (Lombardia Region) in 2002, in Sardegna Region in 2013, and in the Emilia-Romagna Region caused by the Secchia (2014) and Enza (2017) Rivers. The analysis of these data lead to a better knowledge of the types of losses suffered by economic activities, to a representation of damage composition and to the computation of reference damage values. Such a result supports the identification of the more vulnerable elements within the business sector as well as the estimation of the order of magnitude of potential damage, orienting modellers’ and decision-makers’ choices.
