chua-dynamicevolving-2011.pdf (408.75 kB)
Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling
conference contribution
posted on 2011-01-01, 00:00 authored by A Talei, Lloyd ChuaLloyd Chua, C QuekDynamic 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.
History
Event
34th IAHR World Congress, 33rd Hydrology and Water Resources & 10th Hydraulics in Water Engineering. Combined Conference (2011 : Brisbane, Queensland)Pagination
1514 - 1521Publisher
[The Conference]Location
Brisbane, QueenslandPlace of publication
Brisbane, Qld.Start date
2011-06-26End date
2011-07-01ISBN-13
9780858258686Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2011, International Association of Hydraulic Engineering and ResearchTitle 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 AustraliaUsage metrics
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