A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction

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dc.contributor.authorZhou, Y.
dc.contributor.authorWu, W.
dc.contributor.authorNathan, R.
dc.contributor.authorWang, Q.J.
dc.date.issued2021
dc.description.abstractTraditional approaches to inundation modelling are computationally intensive and thus not well suited to assessing the uncertainty involved in estimating flood inundation surfaces for planning, design and forecasting purposes. In this study, a rapid flood inundation modelling framework is developed, consisting of a novel spatial reduction and reconstruction (SRR) approach and a deep learning (DL) modelling component. The SRR approach is developed to reduce computational cost by identifying representative locations of inundation surfaces where water levels are simulated using DL models, and to efficiently reconstruct inundation surfaces based on simulated water level information. The DL model includes a built-in input selection layer to simplify the model development process, and a Long Short-Term Memory layer for time series modelling. The accuracy and efficiency of the SRR-DL framework is assessed by application to a real-world river system where the inundation of over 3 million grid cells can be simulated in 4 s.
dc.description.statementofresponsibilityYuerong Zhou, Wenyan Wu, Rory Nathan, Quan J. Wang
dc.identifier.citationEnvironmental Modelling and Software, 2021; 143
dc.identifier.doi10.1016/j.envsoft.2021.105112
dc.identifier.issn1364-8152
dc.identifier.issn1873-6726
dc.identifier.orcidWu, W. [0000-0003-3907-1570]
dc.identifier.urihttps://hdl.handle.net/2440/132427
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/LE170100200
dc.rights© 2021 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.envsoft.2021.105112
dc.subjectFlood inundation modelling; deep learning; long short-term memory; spatial reduction
dc.titleA rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction
dc.typeJournal article
pubs.publication-statusPublished

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