Optimization and design of Adaptive Neuro-Fuzzy Inference System using Particle Swarm Optimization and Fuzzy C-Means Clustering to predict the scour after bucket spillway

Additionally, if the materials at downstream of bucket spillway are erodible, the ogee spillway is likely to overturn by the time. Therefore, the prediction of the scour after bucket spillway is pretty important. In this study, the scour depths at downstream of bucket spillway are modeled using a new meta-heuristic model. This model is developed by combination of the Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Fuzzy C-Means Clustering (FCM). In addition, to assess the performance of meta-heuristic models, the Monte Carlo simulations (MCs) are used. Also in this paper, the k-fold Cross Validation is used for examination of the models ability. Moreover, the superior model was introduced using analyzing the numerical results. The model predicted the scour depth in terms of all input parameters. The model estimated the scour at downstream of bucket spillway with reasonable accuracy. For example, the mean absolute percentage error and root mean square error for this model were obtained 7.544 and 0.189, respectively. In addition, the superior model was compared with ANFIS model that analyzing showed the compound model had more accuracy.