ANFIS-Based Approach for Predicting the Scour Depth at Culvert Outlets

Abstract

The processes involved in the local scour at culverts are so complex and that makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of adaptive neurofuzzy inference system (ANFIS) to estimate the scour depth at culvert outlets. The data sets of laboratory measurements were compiled from published literature and used to train the ANFIS network. The developed network was validated by using the observations that were not involved in training. The performance of ANFIS was found to be more effective (R2=0.94) when compared with the results of regression equations and artificial neural networks modeling in predicting the scour depth at culvert outlets (R2=0.78). Further work is required to collect field data of scour at culvert outlets to train the genetic programming approach and validate its usefulness.