Development of BEEMD Based Smoothing Algorithm for Topography and a 2D Storage Cell Based Flood Inundation Model
Date Issued
2015
Date
2015
Author(s)
Koh, Yen Hui
Abstract
As rapid population growth and land-use changes increasingly exposed human beings to a greater degree of flood hazards, disaster prevention and mitigation projects have heavily applied numerical modelling methods to assess the risks and impacts at different temporal and spatial scales. Among the wide range of models available, 2D hydrodynamic models are probably the most common tools applied in investigating flood events, owing to its rigorous physical basis and mathematics foundation. However, the presence of highly nonlinear derivatives in the momentum equations have often found triggering numerical instabilities when the model is applied over a complex topography of high curvature variation. In this study, two different strategies are proposed to approach the difficulty. The first method applied is to reduce the local scale curvature through topography smoothing. Here a bi-dimensional ensemble empirical mode decomposition (BEEMD) based smoothing algorithm, namely the Fast and Adaptive Bi-dimensional Ensemble Empirical Mode Decomposition (FABEEMD), is introduced. This new technique is an improvement of Bhuiyan’s work in which pairs of positive and negative signed white noise sets are added into the signal during each iteration of FABEEMD. The introduction of white noise conjugate pairs has resolved the difficulties of mode-mixing and extrema lacking as encountered in the original framework while preserving the extracted bi-dimensional intrinsic mode function (BIMFs) noise-free. As a result, only a small ensemble is required in FABEEMD, enabling the algorithm to decompose any size economically without sacrificing the fidelity. The required smoothed topography is then constructed by reassembling the low frequency components upon the model toleration on surface roughness. The decomposition shows that the proposed method consistently performed much better than the original framework in distinguishing and extracting the local features of different surface roughness. As the noise amplitude increased, macro topographical variation is observed gradually shifting from high to low frequency components, giving the latter a more detailed depiction of the surface. This enables the smoothing to adapt to the surface roughness requirement of the flood simulation model. The second strategy adopted in this work is to develop a simplified flood inundation model by replacing the shallow water equations with Manning’s formula to omit the nonlinear derivatives from momentum computation. The computational domain are spatially discretized into a Triangulated Irregular Network (TIN) with each element being treated as a storage tank. To spatially distribute the runoff with 1D uniform flow formula, we incorporate the Manning’s formula with a multiple flow direction (MFD) framework to form a two steps algorithm in which the total outflow of an element is first computed through weighted averaging the variables required in Manning’s formula, then partitioned into directional components according to their corresponding hydraulic gradients. For urban flooding, underground sewer system and rainwater harvesting are incorporated with the model to provide more reliable simulation. The model was applied to Linbian River watershed during Typhoon Morakot of August 2009 and an idealized urban terrain inundated by a designed rainstorm. In the former, the model has shown satisfactory results in terms of inundation extent and depth by verifying against in-situ observation and the simulation of a 2D hydrodynamic model. For urban flood modeling, the simulation is shown reasonable and stable. In both cases, 0% of mass loss was achieved.
Subjects
Bi-dimensional ensemble empirical mode decomposition (BEEMD)
Topography smoothing
Flood inundation simulation
Storage cell approach
Multiple flow direction algorithm (MFD)
SDGs
Type
thesis
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