Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

Abstract

Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results; however, they are computationally costly and therefore unsuitable for many real time applications and uncertainty analysis that requires a large number of model realizations. Therefore, the present study aims to (i) develop emulators based on SVR and ANN as an alternative for predicting the 100-year flood water level, (ii) improve the performance of the emulators through dimensionality reduction techniques, and (iii) assess the required training sample size to develop an accurate emulator. Our results indicate that SVR based emulator is a fast and reliable alternative that can predict the water level accurately. Moreover, the performance of the models can improve by identifying the most influencing input variables and eliminating redundant inputs from the training process. The findings in this study suggest that the training data size equal to 70% (or more) of data results in reliable and accurate predictions.

Keywords

• emulators
• artificial neural network
• support vector regression
• training set size
• error structure