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Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Talei, A., Chua, L.H.C. and Quek, C. 2011, Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling, in Proceedings of the 34th IAHR World Congress / 33rd Hydrology and Water Resources Symposium : 10th Conference on Hydraulics in Water Engineering, 26 June - 1 July 2011, Brisbane Australia, [The Conference], Brisbane, Qld., pp. 1514-1521.

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Title Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling
Author(s) Talei, A.
Chua, L.H.C.ORCID iD for Chua, L.H.C. orcid.org/0000-0003-2523-3735
Quek, C.
Conference name 34th IAHR World Congress, 33rd Hydrology and Water Resources & 10th Hydraulics in Water Engineering. Combined Conference (2011 : Brisbane, Queensland)
Conference location Brisbane, Queensland
Conference dates 26 Jun.-1 Jul. 2011
Title of proceedings Proceedings of the 34th IAHR World Congress / 33rd Hydrology and Water Resources Symposium : 10th Conference on Hydraulics in Water Engineering, 26 June - 1 July 2011, Brisbane Australia
Editor(s) [Unknown]
Publication date 2011
Conference series IAHR World Congress, Hydrology and Water Resources & Hydraulics in Water Engineering Combined Conference
Start page 1514
End page 1521
Total pages 8
Publisher [The Conference]
Place of publication Brisbane, Qld.
Summary Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.
ISBN 9780858258686
Language eng
Field of Research 059999 Environmental Sciences not elsewhere classified
Socio Economic Objective 970105 Expanding Knowledge in the Environmental Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, International Association of Hydraulic Engineering and Research
Persistent URL http://hdl.handle.net/10536/DRO/DU:30063690

Document type: Conference Paper
Collections: School of Engineering
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.