title: Prediction of scour below submerged pipeline crossing a river using ANN.
abstract: The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.
authors: River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Pulau Pinang, Malaysia. redacazamath@eng.usm.my
authors: H M
authors: HM
authors: Azamathulla
authors: Nor Azazi
authors: NA
authors: Zakaria
mesh_terms: D015233:Models, Statistical; D016571:Neural Networks, Computer; D045483:Rivers; D012498:Sanitary Engineering