Accelerating Predictions of Morphological Bed Evolution by Combining Numerical Modelling and Artificial Neural Networks

Abstract

Process-based models have been employed extensively in the last decades for the prediction of coastal bed evolution in the medium term (1–5 years), under the combined action of waves and currents, due to their ability to resolve the dominant coastal processes. Despite their widespread application, they are associated with high demand for computational resources, rendering the annual prediction of the coastal bed evolution a tedious task. To combat this, wave input reduction methods are generally employed to reduce the sheer amount of sea-states to be simulated to assess the bed level changes. The purpose of this research is to further expand on the concept of input reduction methods by presenting a methodology combining numerical modelling and an Artificial Neural Network (ANN). The trained ANN is tasked with eliminating wave records unable to initiate sediment motion and hence further reduce the required computational times. The methodology was implemented in both an idealized and a real-field case study to examine the sensitivity, and produced very satisfactory predictions of the rates of bed level change, with respect to a benchmark simulation containing a very detailed wave climate. The obtained results have strong implications for further accelerating the demanding morphological simulations while enhancing the reliability and accuracy of model predictions.