The use of artificial neural networks to analyze and predict alongshore sediment transport
By Barend van Maanen, Giovanni Coco, K. Bryan, B. Gerben Ruessink. 2010
46 citations recorded by the source corpus.
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 (erms=0.43) outperforms the commonly used Bailard (1981) formula (erms=1.63), even when this formula is calibrated (erms=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 (erms=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.
Subfields: Earth-Surface Processes, Ecology, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/4a0a88269843d584ce3a6d12b7c3dd6e021e1825
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In Earth-Surface Processes
In Ecology
In Environmental Engineering
Citations in the corpus
- Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs (2014, methodology): “Due to the high capacity of soft computing methods such as artificial neural network (ANN) and particle swarm optimization (PSO) to analyze complex problems, these methods have been used in various hydraulic problems such as discharge coefficient of lateral weirs [20,22–26], scour depth prediction [”