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Water 2017, 9(9), 688; doi:10.3390/w9090688

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

Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, 20133 Milano, Italy
Dipartimento di Ingegneria Civile, Edile-Architettura e Ambientale, Università degli Studi dell’Aquila, 67100 L’Aquila, Italy
Author to whom correspondence should be addressed.
Received: 18 July 2017 / Revised: 21 August 2017 / Accepted: 5 September 2017 / Published: 8 September 2017
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IN-depth SYnthetic Model for Flood Damage Estimation (INSYDE) is a model for the estimation of flood damage to residential buildings at the micro-scale. This study investigates the sensitivity of INSYDE to the accuracy of input data. Starting from the knowledge of input parameters at the scale of individual buildings for a case study, the level of detail of input data is progressively downgraded until the condition in which a representative value is defined for all inputs at the census block scale. The analysis reveals that two conditions are required to limit the errors in damage estimation: the representativeness of representatives values with respect to micro-scale values and the local knowledge of the footprint area of the buildings, being the latter the main extensive variable adopted by INSYDE. Such a result allows for extending the usability of the model at the meso-scale, also in different countries, depending on the availability of aggregated building data. View Full-Text
Keywords: flood damage assessment; INSYDE; micro-scale; meso-scale flood damage assessment; INSYDE; micro-scale; meso-scale

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Description: INSYDE model is available for download as R open source code, currently hosted on GitHub (

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Molinari, D.; Scorzini, A.R. On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE Model. Water 2017, 9, 688.

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