Artificial neural network correction of remotely sensed sandbar location

Abstract

Abstract This contribution details an accurate method for the remote sensing of submerged sandbar location through the application of an artificial neural network to video data. Lippmann and Holman (Lippmann T.C., Holman, R.A., 1989. Quantification of sandbar morphology: a video technique based on wave dissipation. J. Geophys. Res. 94(C1), 995–1011) have shown that the intensity maxima seen in time-averaged video images of the nearshore zone provide a reasonable proxy for sandbar location. However, recent studies have indicated that there may be a large deviation (up to 30–40 m) between the measured bar position and predictions based on the intensity maximum in the cross-shore direction. These deviations occur largely due to modulations of the breakpoint by the tide, and variations in the incident wave energy. This paper utilises an artificial neural network to model the cross-shore movement in the intensity maximum due to tides and waves. The resulting signal is then removed from the video estimates to provide an accurate estimation of the sand bar location. Unique field data sets collected as part of the Coast3D experiment are used to train and test the neural network model. These data consist of simultaneous measurements of the nearshore bathymetry and video imagery of the double bar system at Egmond aan Zee, the Netherlands. The resulting model yields accurate estimates of sandbar location with residual errors of less than 5 m for the outer bar and less than 10 m for the inner bar. The artificial neural network model is utilised to extend the bathymetric data set for Egmond aan Zee for times when wave conditions were too large to allow physical measurement.