Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Neural network modeling for near wall turbulent flow
Journal of Computational Physics
Expert Systems with Applications: An International Journal
A general regression neural network
IEEE Transactions on Neural Networks
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Cross section geometry of stable alluvial channels usually is estimated by simple inaccurate empirical equations, because of the complexity of the phenomena and unknown physical processes of regime channels. So, the main purpose of this study is to evaluate the potential of simulating regime channel treatments using artificial neural networks (ANNs). The process of training and testing of this new model is done using a set of available published filed data (371 data numbers). Several statistical and graphical criterions are used to check the accuracy of the model in comparison with previous empirical equations. The multilayer perceptron (MLP) artificial neural network was used to construct the simulation model based on the training data using back-propagation algorithm. The results show a considerably better performance of the ANN model over the available empirical or rational equations. The constructed ANN models can almost perfectly simulate the width, depth and slope of alluvial regime channels, which clearly describes the dominant geometrical parameters of alluvial rivers. The results demonstrate that the ANN can precisely simulate the regime channel geometry, while the empirical, regression or rational equations can't do this. The presented methodology in this paper is a new approach in establishing alluvial regime channel relations and predicting cross section geometry of alluvial rivers also it can be used to design stable irrigation and water conveyance channels.