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R. Trent, A. Molinas and N. Gagarain, “An Artificial Neural Networks for Computing Sediment Estimation of Scour Below Spillways Using Neural Networks,” Proceedings of ASCE Conference on Hydraulic Engineering, San Francisco, CA, 1993b, pp. 1049-1054.

has been cited by the following article:

• TITLE: Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill

  AUTHORS: Homayoon Seyed Rahman, Keshavarzi Alireza, Gazni Reza

  KEYWORDS: Artificial Neural Network, Scour Depth, Ordinary Kriging, Inverse Distance Weighting, Bridge Piers,

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.10, November 19, 2010

  ABSTRACT: This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.