| 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 (Issue): | 21(8) |
| 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 and/or data comparison |
| Secondary Application Category: | hydrologic assessment |
| Watershed Description: | 501.79 km^2 Mae Tuen and 285.39 km^2 Ohinemuri Rivers, 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. |