publication . Article . 2006

Optimising training data for ANNs with Genetic Algorithms

R. G. Kamp; R. G. Kamp; H. H. G. Savenije;
Open Access English
  • Published: 01 Jan 2006 Journal: Hydrology and Earth System Sciences (issn: 1027-5606, eissn: 1607-7938, Copyright policy)
  • Publisher: Copernicus Publications
Abstract
Artificial Neural Networks (ANNs) have proved to be good modelling tools in hydrology for rainfall-runoff modelling and hydraulic flow modelling. Representative datasets are necessary for the training phase in which the ANN learns the model's input-output relations. Good and representative training data is not always available. In this publication Genetic Algorithms (GA) are used to optimise training datasets. The approach is tested with an existing hydraulic model in The Netherlands. An initial trainnig dataset is used for training the ANN. After optimisation with a GA of the training dataset the ANN produced more accurate model results.
Subjects
free text keywords: Technology, T, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, G, Environmental sciences, GE1-350
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