Evolutionary neural network modeling for describing rainfall-runoff process

Nazemi, Alireza, author
Poorkhadem N., Hossein, author
Akbarzadeh, T., author
Mohammad, R., author
Hosseini, Seyed Mahmood, author
Colorado State University, publisher
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Since the last decade, several studies have shown the ability of Artificial Neural Networks (ANNs) in modeling of rainfall-runoff process. From methodological viewpoint, ANN belongs to more general paradigm, i.e., soft computing or computational intelligence, in which independent methodologies, mostly Fuzzy Logic (FL), ANN, and Genetic Algorithms (GAs), are combined together in order to provide an intelligent behavior in computational frameworks. Consequently, in the context of rainfall-runoff modeling, this question rises whether hybridization of ANNs with other soft computing-related methodologies improves the overall performance of modeling or not. In this study, based on the idea of structure and/or parameter identification of ANNs with GAs, the evolutionary neural networks modeling paradigm is examined for describing the rainfall-runoff process. One of the benchmark data set of current literature, i.e., Leaf River basin (near Collins, Mississippi) data set, is used for simulation. The results show that on the one hand, the overall accuracy is improved; but one the other hand, in evolutionary neural modeling, the computational time is increased significantly. Hence the modeler may be faced with a trade-off problem between accuracy and computational difficulties which may have different importance in a particular rainfall-runoff problem.
Includes bibliographical references.
23rd annual AGU hydrology days was held on March 31 - April 2, 2003 at Colorado State University.
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