Journal article

Artificial neural network based hybrid modeling approach for flood inundation modeling

Shuai Xie, Wenyan Wu, Sebastian Mooser, QJ Wang, Rory Nathan, Yuefei Huang



Flood inundation models are important tools in flood management. Commonly used flood inundation models, such as hydrodynamic or simplified conceptual models, are either computationally intensive or cannot simulate the temporal behavior of floods. Therefore, emulation models based on data-driven methods, such as artificial neural networks (ANNs), have been developed. However, the performance of ANN models, like any other data-driven models, is limited by available data and will not perform well in data-sparse regions. In this study, we developed an ANN-based hybrid modeling approach to improve model performance in data-sparse regions by leveraging better model performance in data-rich regions..

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Awarded by National Natural Science Foundation of China

Awarded by China Scholarship Council

Funding Acknowledgements

The authors would like to thank Hydrology and Risk Consulting (HARC) for providing flood inundation data utilized in this study and SunWater for their permission to use the Burnett River as a case study. The data that support the findings of this study are available online at sutdy was financially supported by the National Natural Science Foundation of China (No. 91647212). Shuai Xie is supported by a program of China Scholarship Council (No. 201806210127) during his visit to The University of Melbourne, where the research is conducted.