Longshore sediment transport estimation using fuzzy inference system
Accurate prediction of longshore sediment transport in the nearshore is essential for the control of shoreline erosion and beach evolution. In this paper, the abilities of an hybrid Adaptive-Network-Based Fuzzy Inference System (ANFIS), a Fuzzy Inference System (FIS), the CERC, Walton-Bruno (WB) and Van Rijn (VR) formulae are used to predict and model longshore sediment transport in the surf zone. The ANFIS and FIS models are established using the wave period, wave breaking angle and the significant breaking wave height and antecedent sediment data. The CERC, WB and VR methods are also applied to the same data. Statistical measures are used to evaluate the performance of the models. The longshore sediment transport rate (LSTR), wave period, wave height, and wave breaking angle at a 4-km-long beach on the central west coast of India are used as case studies. Results indicate that the errors of the ANFIS model in predicting wave parameters are less than those of the CERC, WB, VR and FIS methods. The scatter index of the CERC, WB and VR methods in predicting LSTR is 51.9%, 27.9% and 22.5%, respectively, while the scatter index of the ANFIS model in prediction of LSTR is 17.32%. The comparison of results reveals that the ANFIS model provides higher accuracy and reliability for LSTR estimation than the other techniques.