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

Talei A., Chua L.H.C., Quek C., 34th IAHR World Congress, 26th Jun-1st Jul 2011, Brisbane, Australia., 2011

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.