On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE Model

Molinari, Daniela; Scorzini, Anna Rita

doi:10.3390/w9090688

Cited by 34 publications

(36 citation statements)

References 15 publications

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“…The curves are calibrated on damage micro-data surveyed from a flood event in central Italy (Umbria) (Molinari et al, 2014). Despite the large number of inputs, the model proved to be adaptable to the actual available knowledge of the flood event and building characteristics (Molinari and Scorzini, 2017). The list of explicit inputs accounted for by the INSYDE model is adopted to select the variables accounted for by all MVMs assessed in our analysis (Table 1).…”

Section: Models From the Literaturementioning

confidence: 99%

“…As a final consideration, the accuracy and precision of damage observations are key factors for the correct development of an MVM. This makes synthetic and empirical MVMs better suited for applications at the micro-scale (up to the census block scale; Molinari and Scorzini, 2017), where explanatory variables can be spatially disaggregated. Indeed, the aggregation scale is of primary importance in the application of MVMs: if we can compare our results to those reported in other studies applying similar multivariable approaches on an extensive damage data set (bagging of regression trees), as in Wagenaar et al (2017a) and in Kreibich et al (2017), we observe that our range of uncertainty is drastically smaller.…”

Section: Comparing Model Performancesmentioning

confidence: 99%

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Testing empirical and synthetic flood damage models: the case of Italy

Amadio

Scorzini

Carisi

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set 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), building characteristics (size, type, quality, economic value) and 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 performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.

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Section: Models From the Literaturementioning

confidence: 99%

Section: Comparing Model Performancesmentioning

confidence: 99%

Testing empirical and synthetic flood damage models: the case of Italy

Amadio

Scorzini

Carisi

et al. 2019

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…Exposure can be defined as the depreciated or replacement value of the tangible physical assets in hazard-prone areas [3,4]. Flood risk under climate change and socioeconomic change is a great challenge for flood risk management [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Building Asset Value Mapping in Support of Flood Risk Assessments: A Case Study of Shanghai, China

Wang

et al. 2019

Sustainability

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Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census level). To bridge the gap between census-level BFA data and geo-coded building asset value data, this article introduces a method for building asset value mapping, using Shanghai as an example. This method consists of a census-level BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data, combined with LandScan population density grid data and a financial appraisal of building asset values. Validation with statistical data and field survey data confirms that the method can produce good results, but largely constrained by the resolution of the population density grid used. However, compared with other models with no disaggregation in flood exposure assessment that involves Shanghai, the building asset value mapping method used in this study has a comparative advantage, and it will provide a quick way to produce a building asset value map for regional flood risk assessments. We argue that a sound flood risk assessment should be based on a high-resolution-individual building-based-building asset value map because of the high spatial heterogeneity of flood hazards.Röthlisberger and colleagues [13] compared five building value models for flood risk analysis, and proposed that estimating exposed-building values should be based on individual buildings rather than on areas of land-use types. Higher quality exposure data is needed to perform validations of flood risk models [14].However, for flood risk analysis, quite often a spatial mismatch exists between hazard intensity data (e.g., inundation depth), which are frequently modelled on a high-resolution raster level, and exposure data, which are usually only available at coarse census units (e.g., counties) or aggregated land-use/land cover classes [14][15][16][17]. Flood risk assessments often invest much more in hazard modelling of water depths or inundation areas at a spatially explicit raster level [18,19], while only a limited number of studies have explicitly focused on the estimations of assets and their disaggregation to overcome the spatial mismatch between the quality of hazard and exposure data [20][21][22][23]. Meanwhile, the quality of the exposure data is one of the most important uncertainties in flood risk assessments [24][25][26][27]. Because of the high spatial resolution of water inundation depth estimation, which is mainly a function of topography [28,29], flood risk assessments are much more sensi...

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“…At the same time, flood management decision making has placed higher demands on complex and precise flood information acquisition and applications [29,30]. These new developments have made it possible to better evaluate small-scale flood damage [20,21,31] and develop new flood damage evaluation models such as INSYDE [32].…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Flood-Related Building Damage Evaluation Method Using Airborne LiDAR Data and 2-D Hydraulic Model

Shen

Qian

Chen

et al. 2019

Water

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The evaluation of building damage is of great significance for flood management. Chinese floodplains usually contain small- and medium-sized towns with many other scattered buildings. Detailed building information is usually scarce, making it difficult to evaluate flood damage. We developed an evaluation method for building damage by using airborne LiDAR data to obtain large-area, high-precision building information and digital elevation models (DEMs) for potentially affected areas. These data were then used to develop a two-dimensional (2-D) flood routing model. Next, flood loss rate curves were generated by fitting historical damage data to allow rapid evaluation of single-building losses. Finally, we conducted an empirical study based on the Gongshuangcha detention basin in China’s Dongting Lake region. The results showed that the use of airborne LiDAR data for flood-related building damage evaluation can improve the assessment accuracy and efficiency; this approach is especially suitable for rural areas where building information is scarce.

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On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE Model

Cited by 34 publications

References 15 publications

Testing empirical and synthetic flood damage models: the case of Italy

Testing empirical and synthetic flood damage models: the case of Italy

Building Asset Value Mapping in Support of Flood Risk Assessments: A Case Study of Shanghai, China

A Quantitative Flood-Related Building Damage Evaluation Method Using Airborne LiDAR Data and 2-D Hydraulic Model

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