Using an integrated fuzzy inference system and artificial neural network to forecast daily discharge
Given the nonlinearity and uncertainty in the rainfall-runoff process, estimating or predicting hydrologic data often encounters tremendous difficulty. This study applied fuzzy theory to create a daily flow forecasting modeL To improve the time-consuming definition process of membership function, wh...
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| Outros Autores: | , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2015
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.5/10014 |
| País: | Portugal |
| Oai: | oai:www.repository.utl.pt:10400.5/10014 |
| Resumo: | Given the nonlinearity and uncertainty in the rainfall-runoff process, estimating or predicting hydrologic data often encounters tremendous difficulty. This study applied fuzzy theory to create a daily flow forecasting modeL To improve the time-consuming definition process of membership function, which is usually concluded by a trial-and-error approach, this study designated the membership function by artificial neural network {ANN} with either a supervised or unsupervised learning procedure. The supervised learning was processed by the adaptive network based fuzzy inference system {ANFIS}, while the unsupervised learning was created by fuzzy and self-organizing map {SOMFIS}. The results indicate that the ANFIS method under increment flow data could provide more precise results for daily flow forecasting. |
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