Author(s): Yang Liu; Sylvain Neelz; Gareth Pender

Linked Author(s): Gareth Pender

Keywords: Flooding; Two-Dimensional Modelling; Emulation; V-Support VectorMachine; Particle Swarm Optimisation

Abstract: Full two dimensional (2D) hydrodynamic models have proven to be successful in a wide of applications. The limitation of using full 2D models is their expensive computational requirement. The flood risk analysis and model uncertainty analysis usually need to run the numerical model and evaluate the performance thousands of times. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as v-SVR-CGM, is presented for solving computationally expensive simulation problems. The concept of v-SVR-CGM will be demonstrated via a small number of fine grid model (FGM) runs using v-SV regression. The approximation model is performed in predicting the form of results of FGM instead of running the time consuming FGM. This approach can substantially reduce computational running time without loss of accuracy of FGM. The simulation results suggest that the proposed method is able to achieve good predictive results (water level and velocity) as well as provide considerable savings in computer time.

DOI:

Year: 2009