Rainfall-runoff modelling in a semi-urbanized catchment using self-adaptive Fuzzy Inference Network

Conventional neuro-fuzzy systems used for rainfall-runoff (R-R) modelling generally employ offline learning in which the number of rules and rule parameters need to be set by the user in calibration stage. This make the rule-base fixed and incapable of being adaptive if some rules become inconsistent over time. In this study, the Self-adaptive Fuzzy Inference Network (SaFIN) is used for R-R application. SaFIN benefits from an adaptive learning mechanism which allows it to remove inconsistent and obsolete rules over time. SaFIN models are developed to capture the R-R process in two catchments including Dandenong located in Victoria, Australia, and Sungai Kayu Ara catchment in Selangor, Malaysia. Models’ performance aer then compared with the ANFIS, ARX, and physical models. Results show that SaFIN outperforms ANFIS, ARX, and physical models in simulating runoff for both low and peak flows. This study shows the good potential of using SaFIN in R-R modelling application.