Upskilling low-fidelity hydrodynamic models of flood inundation through
spatial analysis and Gaussian Process learning
Abstract
Accurate flood inundation modelling using a complex high-resolution
hydrodynamic (high-fidelity) model can be very computationally
demanding. To address this issue, efficient approximation methods
(surrogate models) have been developed. Despite recent developments,
there remain significant challenges in using surrogate methods for
modelling the dynamical behaviour of flood inundation in an efficient
manner. Most methods focus on estimating the maximum flood extent due to
the high spatial-temporal dimensionality of the data. This study
presents a hybrid surrogate model, consisting of a low-resolution
hydrodynamic (low-fidelity) and a Sparse Gaussian Process (Sparse GP)
model, to capture the dynamic evolution of the flood extent. The
low-fidelity model is computationally efficient but has reduced accuracy
compared to a high-fidelity model. To account for the reduced accuracy,
a Sparse GP model is used to correct the low-fidelity modelling results.
To address the challenges posed by the high dimensionality of the data
from the low- and high-fidelity models, Empirical Orthogonal Functions
(EOF) analysis is applied to reduce the spatial-temporal data into a few
key features. This enables training of the Sparse GP model to predict
high-fidelity flood data from low-fidelity flood data, so that the
hybrid surrogate model can accurately simulate the dynamic flood extent
without using a high-fidelity model. The hybrid surrogate model is
validated on the flat and complex Chowilla floodplain in Australia. The
hybrid model was found to improve the results significantly compared to
just using the low-fidelity model and incurred only 39% of the
computational cost of a high-fidelity model.