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1. Surrogate Modeling in Predicting Fine Sediment transportation along the Dutch Coastal Area | |||
Chu Kai | |||
Hydraulic Engineering 24 July 2009 | |||
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Abstract:The use of both process-based model and data driven model (DDM) in simulating sediment processes have shown to be useful by previous research. However, both approaches have disadvantages. This paper explored several data driven methods to build simple models in predicting SPM based on output from the process-based models. Artificial neural network (ANN) is adopted as surrogate model to predict suspended particulate matter (SPM) concentration in the Southern North Sea. Surrogate model is essentially a simple and fast ‘model of the model’. The simulation by surrogate models is acceptable and simulation time reduces dramatically. Surrogate models are also built with linear regression method which refers to ‘parsimonious model’. Parsimonious model is the simplest feasible model with the fewest possible number of variables It requires less computation time, the simulation is transparent and results are easy to interpret. | |||
TO cite this article:Chu Kai. Surrogate Modeling in Predicting Fine Sediment transportation along the Dutch Coastal Area[OL].[24 July 2009] http://en.paper.edu.cn/en_releasepaper/content/34067 |
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