The use of artificial neural networks to analyze and predict alongshore sediment transport

B. van Maanen*, G. Coco, K. R. Bryan, B. G. Ruessink

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

An artificial neural network (ANN) was developed to predict the depth-integrated alongshore suspended sediment transport rate using 4 input variables (water depth, wave height and period, and alongshore velocity). The ANN was trained and validated using a dataset obtained on the intertidal beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between observations and predictions was calculated to show that, for this specific dataset, the ANN (epsilon(rms)=0.43) outperforms the commonly used Bailard (1981) formula (epsilon(rms)=1.63), even when this formula is calibrated (epsilon(rms)=0.66). Because of correlations between input variables, the predictive quality of the ANN can be improved further by considering only 3 out of the 4 available input variables (epsilon(rms)=0.39). Finally, we use the partial derivatives method to "open and lighten" the generated ANNs with the purpose of showing that, although specific to the dataset in question, they are not " black-box" type models and can be used to analyze the physical processes associated with alongshore sediment transport. In this case, the alongshore component of the velocity, by itself or in combination with other input variables, has the largest explanatory power. Moreover, the behaviour of the ANN indicates that predictions can be unphysical and therefore unreliable when the input lies outside the parameter space over which the ANN has been developed. Our approach of combining the strong predictive power of ANNs with " lightening" the black box and testing its sensitivity, demonstrates that the use of an ANN approach can result in the development of generalized models of suspended sediment transport.

Original languageEnglish
Pages (from-to)395-404
Number of pages10
JournalNonlinear Processes in Geophysics
Volume17
Issue number5
DOIs
Publication statusPublished - 2010

Funding

BvM and GC funded by the (New Zealand) Foundation for Research, Science and Technology (contracts CF102203 and WRHC102). BGR acknowledges funding by the Netherlands Organisation for Scientific Research (NWO) under contract 864.04.007.

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