Alongshore sediment transport rate –measurement and comparison with empirical formulas and an Artificial Neural Network (ANN) model

Abstract

The rate from alongshore sediment transport in the surf zone depends on the product of the local wave height and mean alongshore current speed. The aim of this article was to predict the alongshore sediment transport rate using a semi-empirical application of Artificial Neural Network (ANN) on the south coast of the Caspian Sea. This study reports the measurements of the alongshore sediment transport rate performed in the surf zone of the Noor coastal area located in the southern part of the Caspian Sea from September 2011 to June 2012. Further, alongshore sediment transport rates have been estimated by different famous semi-empirical formulas. On the other hand, an artificial neural network model was trained using three predominant parameters of sediment transport formulas including wave-breaking height (Hb), surf zone width (W), and alongshore current velocity (V). ANN models were able to show hidden laws of natural phenomena such as the sediment transport process. The results of ANN and some sediment transport rate formulas concerning alongshore sediment transport rate were compared with corresponding measured values. Sediment transport is still an evolving science because it depends on complex processes. It is worth mentioning that some of these processes have not been measured or fully understood. Therefore, it is necessary for engineers to pay attention to the fact that even the best forecasts available in the field of sediment transport have a wider margin of error than the forecasts expected in other disciplines and fields of science and engineering. The results show that the estimated value of alongshore sediment transport rate by Coastal Engineering Research Center (CERC), Walton and Bruno, Kamphuis formulas