Bayesian Data-Driven approach enhances synthetic flood loss models

Sairam, Nivedita, Schröter, Kai, Carisi, Francesca, Wagenaar, Dennis, Domeneghetti, Alessio, Molinari, Daniela, Brill, Fabio, Priest, Sally J. ORCID: https://orcid.org/0000-0003-2304-1502, Viavattene, Christophe ORCID: https://orcid.org/0000-0002-4358-5411, Merz, Bruno and Kreibich, Heidi (2020) Bayesian Data-Driven approach enhances synthetic flood loss models. Environmental Modelling and Software, 132 , 104798. ISSN 1364-8152 [Article] (doi:10.1016/j.envsoft.2020.104798)

Preview

PDF - Final accepted version (with author's formatting) Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0. Download (603kB) | Preview

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.

Actions (login required)

View Item

Statistics

DownloadsShow export options

Export as CSVXMLJSON

Activity Overview

6 month trend

100Downloads

6 month trend

137Hits

Additional statistics are available via IRStats2.