DYNAMIC EVOLVING NEURAL FUZZY INFERENCE SYSTEMS FOR EVENT-BASED RAINFALL-RUNOFF MODELING IN A LARGE TROPICAL CATCHMENT

Abstract

Population growth in fast developing countries such as Malaysia leads to more demand for infrastructures which in turn may gradually transform agricultural or forest landscapes to built-up areas. This change has significant impact on hydrologic processes which can lead to an increase in both magnitude and frequency of floods in urban areas. To date several physically-based models are developed to capture the rainfall-runoff process; however, they require significant number of parameters which could be difficult to be measured or estimated. Recently, neuro-fuzzy systems (NFS) which are well-known for their ability in simulating nonlinear complex systems have been widely used in hydrological time series modeling and prediction. Online learning and rule evolving mechanisms of Dynamic Evolving Neural-Fuzzy Inference Systems (DENFIS) are the two capabilities that make it suitable to be used as a tool for rainfall-runoff modeling. The results obtained by DENFIS model were compared with an autoregressive model with exogenous inputs (ARX) as a bench mark. Results revealed that DENFIS has a good potential to be used as a rainfall-runoff modeling tool.