SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Multimodel approach using neural networks and symbolic regression to combine the estimated discharges of Rainfall-Runoff models 
Authors:Phukoetphim, P., A.Y. Shamseldin and K. Adams 
Year:2016 
Journal:Journal of Hydrologic Engineering 
Volume: 
Pages: 
Article ID: 
DOI:10.1061/(ASCE)HE.1943-5584.0001332 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:model comparison 
Secondary Application Category:hydrologic assessment 
Watershed Description:501.79 km^2 Mae Tuen River, and the 285.39 km^2 Ohinemuri River, located respectively in northwest Thailand and the northern part of the North Island of New Zealand 
Calibration Summary: 
Validation Summary: 
General Comments: 
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. 
Language:English 
Keywords:Multimodel approach; Neural networks; Gene expression programming; Combination methods; Multilayer perceptron neural network; Radial basis function neural network.