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Neural Network Modelin of Nearshore Sandbar Behavior

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4 Author(s)
L. Pape ; Institute for Marine and Atmospheric research Utrecht, Department of Physical Geography, Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, Netherlands. phone: +31 30 2532982; fax: +31 30 2531145; email: ; B. G. Ruessink ; M. A. Wiering ; I. L. Turner

The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly predicted using process-based models. These models are autoregressive and require offshore wave characteristics as input, properties that find their neural network equivalent in the NARX (nonlinear auto-regressive model with exogenous input) architecture. Earlier literature results suggest that the evolution of sandbars depends nonlinearly on the wave forcing and that the sandbar position at a specific moment contains 'memory', that is, time-series of sandbar positions show dependencies spanning relatively long time periods. Using observations of an outer sandbar collected daily for about 3.5 years at the double-barred Surfers Paradise, Gold Coast, Australia we find, however, little difference in performance between a NARX, an autoregressive multilayer perceptron (without long-term dependencies), and a linear NARX. It is uncertain whether these results generalize to the inner Gold Coast bar or to other field sites.

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The 2006 IEEE International Joint Conference on Neural Network Proceedings

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