E3S Web Conf.
Volume 7, 20163rd European Conference on Flood Risk Management (FLOODrisk 2016)
|Number of page(s)||7|
|Section||Hazard analysis and modelling|
|Published online||20 October 2016|
Applying emulators for improved flood risk analysis
1 HR Wallingford, Howbery Business Park, Wallingford, Oxfordshire OX10 8BA, UK
2 The University of Sheffield, School of Mathematics and Statistics, Sheffield, South Yorkshire S10 2TN, UK
a Corresponding author: S.Malde@hrwallingford.com
Flood risk analysis often involves the integration of multivariate probability distributions over a domain defined by a consequence function. Often, solutions of this risk integral involves Monte-Carlo sampling techniques, whereby 1000’s of potential flood events are generated. It is necessary to evaluate the consequence of flooding for each sampled event. A significant computational time is required in running flood related physical process models, making it computationally impractical to evaluate flood risk using this approach. To overcome the computational challenges, this paper focusses on the Gaussian Process Emulator (GPE) meta-modelling approach. Traditionally, a “look-up table” method is used when a large number of simulations from a numerical model are required. This approach typically involves simulating conditions defined across a regular matrix, and then linearly interpolating intermediate conditions. In this paper we compare a traditional “look-up table” approach to the GPE and analyse their performance in approximating SWAN wave transformation model. In both cases, selecting an appropriate training design set is important and is taken into consideration in the analysis. The analysis shows that the GPE approach requires significantly fewer SWAN runs to obtain similar (or better) accuracies, enabling a substantial reduction in computation time, hence aiding the practicality of Monte-Carlo sampling techniques in advanced flood risk modelling.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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