Multimodel Approach Using Neural Networks and Symbolic Regression to Combine the Estimated Discharges of Rainfall-Runoff Models
Publication: Journal of Hydrologic Engineering
Volume 21, Issue 8
Abstract
The aim of this study is to compare the performance of a symbolic regression combination method based on gene expression programming (GEP) with different neural network combination methods when used in the development of multimodel systems. The two different neural network combination methods used in this study are the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). The methods were used to combine the results from different types of rainfall-runoff models to test the multimodel combination system in catchments located in Thailand and New Zealand. Comparison of the results revealed that the GEP performed better than neural network methods in the case of the catchment located in New Zealand. Nevertheless, the RBFNN method outperformed the GEP and the MLPNN combination method in the case of the catchment located in Thailand. However, which combination method produces better results in the multimodel combination is not conclusive. The results suggest that the selection of the best combination method to be used in conjunction with the multimodel approach may depend on the catchment type.
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Journal of Hydrologic Engineering
Volume 21 • Issue 8 • August 2016
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© 2016 American Society of Civil Engineers.
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Received: Jan 30, 2015
Accepted: Oct 21, 2015
Published online: Mar 24, 2016
Published in print: Aug 1, 2016
Discussion open until: Aug 24, 2016
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