摘要：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.
