A pseudospectral collocation approach for flood inundation modelling with random input fields
In this study, an efficient framework of pseudospectral collocation approach combined with the generalized polynomial chaos (gPC) and Karhunen-Loevè expansion (gPC/KLE) was introduced to examine the flood flow fields within a two-dimensional flood modelling system. In the proposed framework, the het...
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sg-ntu-dr.10356-857922022-07-22T06:38:44Z A pseudospectral collocation approach for flood inundation modelling with random input fields Qin, Xiao Sheng Huang, Yuefei School of Civil and Environmental Engineering Environmental Process Modelling Centre Nanyang Environment and Water Research Institute Collocation Generalized Polynomial Chaos In this study, an efficient framework of pseudospectral collocation approach combined with the generalized polynomial chaos (gPC) and Karhunen-Loevè expansion (gPC/KLE) was introduced to examine the flood flow fields within a two-dimensional flood modelling system. In the proposed framework, the heterogeneous random input field (logarithmic Manning’s roughness) was approximated by the normalized KLE and the output field of flood flow depth was represented by the gPC expansion, whose coefficients were obtained with a nodal set construction via Smolyak sparse grid quadrature. In total, 3 scenarios (with different levels of input spatial variability) were designed for gPC/KLE application and the results from Monte Carlo simulations were provided as the benchmark for comparison. This study demonstrated that the gPC/KLE approach could predict the statistics of flood flow depth (i.e., means and standard deviations) with significantly less computational requirement than MC; it also outperformed the probabilistic collocation method (PCM) with KLE (PCM/KLE) in terms of fitting accuracy. This study made the first attempt to apply gPC/KLE to flood inundation field and evaluated the effects of key parameters (like the number of eigenpairs and the order of gPC expansion) on model performances. MOE (Min. of Education, S’pore) Published version 2018-07-27T01:48:11Z 2019-12-06T16:10:18Z 2018-07-27T01:48:11Z 2019-12-06T16:10:18Z 2017 Journal Article Huang, Y., & Qin, X. S. (2016). A pseudospectral collocation approach for flood inundation modelling with random input fields. Journal of Environmental Informatics, 30(2), 95-106. 1726-2135 https://hdl.handle.net/10356/85792 http://hdl.handle.net/10220/45280 10.3808/jei.201600339 en Journal of Environmental Informatics © 2017 ISEIS. This paper was published in Journal of Environmental Informatics and is made available as an electronic reprint (preprint) with permission of ISEIS. The published version is available at: [http://dx.doi.org/10.3808/jei.201600339]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 12 p. application/pdf |
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Collocation Generalized Polynomial Chaos Qin, Xiao Sheng Huang, Yuefei A pseudospectral collocation approach for flood inundation modelling with random input fields |
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In this study, an efficient framework of pseudospectral collocation approach combined with the generalized polynomial chaos (gPC) and Karhunen-Loevè expansion (gPC/KLE) was introduced to examine the flood flow fields within a two-dimensional flood modelling system. In the proposed framework, the heterogeneous random input field (logarithmic Manning’s roughness) was approximated by the normalized KLE and the output field of flood flow depth was represented by the gPC expansion, whose coefficients were obtained with a nodal set construction via Smolyak sparse grid quadrature. In total, 3 scenarios (with different levels of input spatial variability) were designed for gPC/KLE application and the results from Monte Carlo simulations were provided as the benchmark for comparison. This study demonstrated that the gPC/KLE approach could predict the statistics of flood flow depth (i.e., means and standard deviations) with significantly less computational requirement than MC; it also outperformed the probabilistic collocation method (PCM) with KLE (PCM/KLE) in terms of fitting accuracy. This study made the first attempt to apply gPC/KLE to flood inundation field and evaluated the effects of key parameters (like the number of eigenpairs and the order of gPC expansion) on model performances. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Qin, Xiao Sheng Huang, Yuefei |
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Article |
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Qin, Xiao Sheng Huang, Yuefei |
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Qin, Xiao Sheng |
title |
A pseudospectral collocation approach for flood inundation modelling with random input fields |
title_short |
A pseudospectral collocation approach for flood inundation modelling with random input fields |
title_full |
A pseudospectral collocation approach for flood inundation modelling with random input fields |
title_fullStr |
A pseudospectral collocation approach for flood inundation modelling with random input fields |
title_full_unstemmed |
A pseudospectral collocation approach for flood inundation modelling with random input fields |
title_sort |
pseudospectral collocation approach for flood inundation modelling with random input fields |
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2018 |
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https://hdl.handle.net/10356/85792 http://hdl.handle.net/10220/45280 |
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1739837405917085696 |