Internet Archive Scholar

Optimising training data for ANNs with Genetic Algorithms

R. G. Kamp, H. H. G. Savenije
2006 Hydrology and Earth System Sciences  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (559.8 kB)
https://web.archive.org/web/20110419122458/http://hal-insu.archives-ouvertes.fr:80/docs/00/30/50/13/PDF/hess-10-603-2006.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL. The file type is application/pdf.

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
more » ... trainnig dataset is used for training the ANN. After optimisation with a GA of the training dataset the ANN produced more accurate model results.
doi:10.5194/hess-10-603-2006 fatcat:6ucmakgnrnaj5dknesdkhdpery
DOAJ Szczepanski
Citation

R. G. Kamp, H. H. G. Savenije. "Optimising training data for ANNs with Genetic Algorithms." Hydrology and Earth System Sciences 10.4 (2006) 603-608