Alongshore Sediment Transport Rate Estimation by an Artificial Neural Network (ANN) Model

The estimation of alongshore sediment transport rate (LSTR) is the most important factor in analyzing the amount of erosion or accretion along a coast. In the present research, an LSTR measurement was done at daily intervals using sediment traps in Noor coastal area, north of Iran, from March 21 to June 22, 2012. The existing empirical relations are linear or exponential regressions based on the observations and measurements data. Based on calculations, the yearly average of sediment transport rate is 928.73 (m3/day) for Noor coastal area. One of the most widely used methods for estimating LSTR, which has advantages compared with others, is setting up and application of an artificial neural network (ANNs) and the present study attempts to develop such a model. Different ANNs with different input configurations and transfer functions were examined. The results reveal that usage of the hyperbolic tangent is better than application of the sigmoid as the transfer functioning. Moreover, the ANN with wave breaking height (), surf zone width (W), and alongshore current velocity (V), as inputs and sediment transport rate (Q) as output configures the best model and predicts more reliably, with higher correlation coefficient, R2, of 0.96, the L.S.T.R among others. Using the ANNs model presented in this research, therefore, the sediment transport rate can be estimated with sufficient accuracy.