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.

Integrated Modeling of Coastal Processes Driven by an Advanced Mild Slope Wave Model

“…It is capable of simulating nearshore currents generated by waves, wind, and tides. The model solves the depth-integrated incompressible Reynolds-averaged Navier-Stokes equations, composed of the following continuity and momentum equations [5]:…”

“…The prediction of coastal morphodynamics at a time span of years to decades [1][2][3][4][5][6], induced by the combined effect of waves and currents, has been at the forefront of coastal engineering research for several decades, with the ultimate goal to thoroughly investigate the complex nearshore processes and mitigate erosion. To combat the adverse effects of erosion, coastal protection works (e.g., breakwaters, groins, and seawalls) have been constructed by engineers focusing predominantly on reducing the incident wave energy.…”

“…The scope of this paper is to present the validation and application of the integration of models, consisting of an irregular and nonlinear mild slope wave model [49,58] coupled with a hydrodynamic [59] and sediment transport model [5], to thoroughly investigate wave transformation, wave-generated hydrodynamics, and sediment transport in coastal regions, with or without coastal works. Of particular importance is the validation of the energy dissipation mechanism for the wave model, constituting the main driver of nearshore processes, as well as a parametric numerical application to assess the effect of wave reflection due to a seawall in longshore hydrodynamics and sediment transport.…”

Sediment Transport Equivalent Waves for Estimating Annually Averaged Sedimentation and Erosion Trends in Sandy Coastal Areas

“…These coastal area models are applicable at a range of spatial scales-from small to macro scale coastal engineering problems. Despite the significant progress that has been made on 3D-and quasi 3D models [1][2][3], prohibitive computational efforts are required when simulating real coastal areas of several kilometers, and this is why 2DH Models [4][5][6] are widely used in real-life coastal engineering challenges. However, even these models usually require small grid cell sizes and small time-steps to resolve the dominant processes and thus they remain computationally intensive, especially when the prediction of morphological changes is required over a longer time than a storm duration, e.g., on an annual basis.…”