Longshore Current Velocities Prediction:
Using A Neural Networks Approach
Author(s)
T. M. Alaboud & M. S. El-Bisy
Abstract
Accurate current conditions prediction and supplement is an important task in the
successful development and management of coastal zone infrastructure.
Longshore current velocities forecasting is currently made by adopting
conventional numerical and analytical models. In fact, longshore current
velocities prediction in the conventional numerical and analytical models require
a large amount information apart from historical wave observations and
topography maps and are complex and tedious to apply specifically when pointforecasts
at specific locations are needed. Therefore, this paper presents an
application of a neural network for forecasting and supplementing the daily and
monthly longshore current velocities. The neural network was trained using back
propagation and cascade correlation algorithms. The data of five stations along
the Damietta promontory on the Egyptian Nile delta coast were used to test the
performance of the neural network model. The results indicated that the neural
network can efficiently forecast longshore current velocities. Neural network
forecasting was also found to be more accurate than traditional statistical timeseries
analysis.
Keywords: longshore current velocities, forecasting, neural networks, ARIMA
model.
1 Introduction
Longshore currents play the main role in sediment transport along the coast. The
longshore current combined with the agitating action of the breaking waves, are
the primary factors in causing sand movement. Studies indicate that the greatest
Keywords
longshore current velocities, forecasting, neural networks, ARIMA
model
Related Book
Other papers in this volume
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