Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.
Keywords: Drainage path delineation; Flood inundation modelling; Flood mapping; Spatial reconstruction; Spatial reduction; Surface hydrology.
© 2021 The Authors. Published by Elsevier B.V.