In this study, the adaptive network-based fuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to estimate the wind- and wave-induced coastal current velocities. The collected data at the Joeutsu-Ogata coast of the Japan Sea were used to develop the models. In the models, significant wave height, wave period, wind direction, water depth, incident wave angle, and wind speed were considered as the input variables; and longshore and cross-shore current velocities as the output variables. The comparison of the models showed that the ANN model outperforms the ANFIS model. In addition, evaluation of the models versus the multiple linear regression and multiple nonlinear regression with power functions models indicated their acceptable accuracy. A sensitivity test proved the stronger effects of wind speed and wind direction on longshore current velocities. In addition, this test showed great effects of significant wave height on cross-shore currents' velocities. It was concluded that the angle of incident wave, water depth, and significant wave period had weaker influences on the velocity of coastal currents.
- cross-shore current
- Joeutsu-Ogata coast
- longshore current
- wind- and wave-induced currents
- First received 29 September 2014.
- Accepted in revised form 27 August 2015.
- © IWA Publishing 2015