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A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers
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Author(s): Jianhua Yang (University of Warwick, UK), Evor L. Hines (University of Warwick, UK), Ian Guymer (University of Warwick, UK), Daciana D. Iliescu (University of Warwick, UK), Mark S. Leeson (University of Warwick, UK), Gregory P. King (University of Warwick, UK)and XuQuin Li (University of Warwick, UK)
Copyright: 2009
Pages: 17
Source title:
Advancing Artificial Intelligence through Biological Process Applications
Source Author(s)/Editor(s): Ana B. Porto Pazos (Coruna University, Spain), Alejandro Pazos Sierra (Coruna University, Spain)and Washington Buño Buceta (Cajal Institute, Spanish Council for Scientific Research, Spain)
DOI: 10.4018/978-1-59904-996-0.ch019
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Abstract
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural network structure and makes the training process more efficient. Once input variables are determined, GNMM processes the data using an MLP with the back-propagation algorithm. The MLP is presented with a series of training examples and the internal weights are adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study data, which has previously been explored by other authors using various methods. By comparing the results generated by GNMM to those presented in the literature, the effectiveness of this methodology is demonstrated.
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