Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 1.321 IF 1.321
  • IF 5-year<br/> value: 1.636 IF 5-year
  • SNIP value: 0.903 SNIP 0.903
  • SJR value: 0.709 SJR 0.709
  • IPP value: 1.455 IPP 1.455
  • h5-index value: 20 h5-index 20
Nonlin. Processes Geophys., 17, 395-404, 2010
© Author(s) 2010. This work is distributed
under the Creative Commons Attribution 3.0 License.
02 Sep 2010
The use of artificial neural networks to analyze and predict alongshore sediment transport
B. van Maanen1,2, G. Coco1, K. R. Bryan2, and B. G. Ruessink3 1National Institute of Water and Atmospheric Research (NIWA), P.O. Box 11-115, Hamilton, New Zealand
2Department of Earth and Ocean Sciences, University of Waikato, Private Bag 3105, Hamilton, New Zealand
3Department of Physical Geography, Faculty of Geosciences, Institute for Marine and Atmospheric Research, Utrecht University, The Netherlands
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 (εrms=0.43) outperforms the commonly used Bailard (1981) formula (εrms=1.63), even when this formula is calibrated (εrms=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 (εrms=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.

Citation: van Maanen, B., Coco, G., Bryan, K. R., and Ruessink, B. G.: The use of artificial neural networks to analyze and predict alongshore sediment transport, Nonlin. Processes Geophys., 17, 395-404, doi:10.5194/npg-17-395-2010, 2010.
Publications Copernicus