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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Flood risk management and adaptation : An efficient framework applied in unsteady-condition flood modeling using sparse grid stochastic col...
An efficient framework applied in unsteady-condition flood modeling using sparse grid stochastic collocation method
An efficient framework based on the stochastic collocation (SG) method and sparse grid quadrature, called sparse grid
stochastic collocation (SGSC) method, is applied in the uncertainty quantification of flood inundation modelling under
joint effect from Manning¡¯s roughness coefficient (n) and hydraulic conductivity (Ks), which are relatively sensitive
parameters in affecting flood simulation results (such as flow velocities and flow depths). In this study, as uncertain
inputs, n is assumed in a uniform distribution and Ks is in a lognormal distribution, respectively, of which the sampling
space is based on the collocation points constructed by sparse grid quadrature. Based on these constructed collocation
points, output fields over the modelling domain can be represented by Lagrange polynomials and their moments can be
obtained by corresponding collocation points and weights. To demonstrate the applicability of the proposed method, a
2D flood inundation case is selected and an appropriate surrogate model by SGSC is built up. The means and standard
deviations of the flow-depth and velocities distributions over the modelling domain are compared with those simulated by
traditional Monte Carlo (MC) simulation. The simulation results show that SGSC approach can efficiently deal with the
multi-uncertainty problems during the flood numerical modelling and significantly reduce the computational burden
caused by repetitive runs of numerical models, which has been a common problem in MC simulation.
File Size : 405,214 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Flood risk management and adaptation
Date Published : 18/08/2015
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