Bayesian Data-Driven approach enhances synthetic flood loss models
Article
Sairam, N., Schröter, K., Carisi, F., Wagenaar, D., Domeneghetti, A., Molinari, D., Brill, F., Priest, S., Viavattene, C., Merz, B. and Kreibich, H. 2020. Bayesian Data-Driven approach enhances synthetic flood loss models. Environmental Modelling and Software. 132. https://doi.org/10.1016/j.envsoft.2020.104798
Type | Article |
---|---|
Title | Bayesian Data-Driven approach enhances synthetic flood loss models |
Authors | Sairam, N., Schröter, K., Carisi, F., Wagenaar, D., Domeneghetti, A., Molinari, D., Brill, F., Priest, S., Viavattene, C., Merz, B. and Kreibich, H. |
Abstract | Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk. |
Research Group | Flood Hazard Research Centre |
Publisher | Elsevier |
Journal | Environmental Modelling and Software |
ISSN | 1364-8152 |
Publication dates | |
Online | 18 Jul 2020 |
01 Oct 2020 | |
Publication process dates | |
Deposited | 12 Aug 2020 |
Accepted | 10 Jul 2020 |
Output status | Published |
Accepted author manuscript | License |
Copyright Statement | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.envsoft.2020.104798 |
Language | English |
https://repository.mdx.ac.uk/item/8908w
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