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Python program for spatial reduction and reconstruction method in flood inundation modelling

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 de...

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Detalles Bibliográficos
Autores principales: Zhou, Yuerong, Wu, Wenyan, Nathan, Rory, Wang, Quan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563644/
https://www.ncbi.nlm.nih.gov/pubmed/34754797
http://dx.doi.org/10.1016/j.mex.2021.101527
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author Zhou, Yuerong
Wu, Wenyan
Nathan, Rory
Wang, Quan J.
author_facet Zhou, Yuerong
Wu, Wenyan
Nathan, Rory
Wang, Quan J.
author_sort Zhou, Yuerong
collection PubMed
description 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%.
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spelling pubmed-85636442021-11-08 Python program for spatial reduction and reconstruction method in flood inundation modelling Zhou, Yuerong Wu, Wenyan Nathan, Rory Wang, Quan J. MethodsX Method Article 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%. Elsevier 2021-09-24 /pmc/articles/PMC8563644/ /pubmed/34754797 http://dx.doi.org/10.1016/j.mex.2021.101527 Text en © 2021 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Method Article
Zhou, Yuerong
Wu, Wenyan
Nathan, Rory
Wang, Quan J.
Python program for spatial reduction and reconstruction method in flood inundation modelling
title Python program for spatial reduction and reconstruction method in flood inundation modelling
title_full Python program for spatial reduction and reconstruction method in flood inundation modelling
title_fullStr Python program for spatial reduction and reconstruction method in flood inundation modelling
title_full_unstemmed Python program for spatial reduction and reconstruction method in flood inundation modelling
title_short Python program for spatial reduction and reconstruction method in flood inundation modelling
title_sort python program for spatial reduction and reconstruction method in flood inundation modelling
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563644/
https://www.ncbi.nlm.nih.gov/pubmed/34754797
http://dx.doi.org/10.1016/j.mex.2021.101527
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