Uncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic sampling

A generalized likelihood uncertainty estimation (GLUE) method incorporating moving least squares (MLS) with entropy for stochastic sampling (denoted as GLUE-MLS-E) was proposed for uncertainty analysis of flood inundation modelling. The MLS with entropy (MLS-E) was established according to the pairs of parameters/likelihoods generated from a limited number of direct model executions. It was then applied to approximate the model evaluation to facilitate the target sample acceptance of GLUE during the Monte-Carlo-based stochastic simulation process. The results from a case study showed that the proposed GLUE-MLS-E method had a comparable performance as GLUE in terms of posterior parameter estimation and predicted confidence intervals; however, it could significantly reduce the computational cost. A comparison to other surrogate models, including MLS, quadratic response surface and artificial neural networks (ANN), revealed that the MLS-E outperformed others in light of both the predicted confidence interval and the most likely value of water depths. ANN was shown to be a viable alternative, which performed slightly poorer than MLS-E. The proposed surrogate method in stochastic sampling is of practical significance in computationally expensive problems like flood risk analysis, real-time forecasting, and simulation-based engineering design, and has a general applicability in many other numerical simulation fields that requires extensive efforts in uncertainty assessment. Copyright (c) 2014 John Wiley & Sons, Ltd.