Effective prediction of scour downstream of ski-jump buckets using artificial neural networks

The main objective of this research was to analyze and quantify the uncertainty of artificial neural network in prediction of scour downstream ski-jump buckets. Hence, at first, three artificial neural network models were developed to predict depth, length, and width of scour hole. Then, Monte-Carlo simulation was applied in the estimates of artificial neural network modeling procedure. The uncertainties were quantified by means of two criteria: 95 percent prediction uncertainty and d-factor. The results of the artificial neural network models showed superior performance of it in comparison with some empirical formulas because of higher correlation coefficient (R 2 > 0.95) and lower error (RMSE < 1.63). The obtained result from uncertainty analysis of the models revealed the satisfactory performance of them. In this procedure it was clarified that the artificial neural network model for length prediction was more reliable than the others with d-factor and 95 percent prediction uncertainty equal to 2.53 and 92, respectively.