Estimation of rip density on intermediate beaches using an extreme learning machine model

Abstract

Rip currents are one of the most well-known and risky flows in coastal areas, and given the limitations and difficulties in field data collection, predicting the varied behavior of these flows is of particular importance. In this study, based on the design of the intermediate beaches, the wave and bed parameters in the coastal zone were extracted as dimensionless ratios of Froude number, wave height, surf zone width and rip channel width by Mike 21/3 numerical model, and were used as input for different approaches of extreme learning machine (ELM) and artificial neural network (ANN). Then, while examining the different beach states over time, the optimum values for statistical criteria in different models were investigated and compared. Next, the effect of each of the available parameters on the rip density was considered, and the results obtained by different models in different climates of the waves were compared with the field results, which showed high level of agreement. The main results of the study revealed that the speed of implementation of different test, training and validation phases in ELM model was 1.5 times faster than ANN model on average. Furthermore, it was shown that the different percentages of the rip density values observed by the numerical model and predicted by different methods of the ELM model were always lower than those of the ANN model. Also, the dispersion of the predicted data by different models of the extreme learning machine, especially hierarchical extreme learning machine (HELM), was less than the dispersion of the data obtained by the other models, which makes the HELM model superior to other soft computing methods in predicting the rip density on intermediate beaches.