A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling.

Talei A., Chua L.H.C., Quek C., Expert Systems with Applications, Vol. 37(12), pp. 7456-7468., 2010

This study presents an application of an Adaptive Network-based
Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in event-based R–R modeling
in order to evaluate the capabilities of this method for a sub-catchment of Kranji basin in Singapore.
Approximately two years of rainfall and runoff data which from 66 separate rainfall events were analyzed
in this study. Two different approaches in the selection criteria for calibration events were adopted and
the performance of an ANFIS R–R model was compared against an established physically-based model
called Storm Water Management Model (SWMM) in R–R modeling. The results of this study show that
the selected neuro-fuzzy-computational technique (ANFIS) is comparable to SWMM in event-based
R–R modeling. In addition, ANFIS is found to be better at peak flow estimation compared to SWMM.