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RAINFALL RUNOFF MODELING USING RADIAL BASIS FUNCTION NEURAL NETWORK FOR SUNGAI TINJAR CATCHMENT, MIRI, SARAWAK

Suhaimi, S1., Rosmina A. Bustami2

Abstract

Artificial Neural Network (ANN) is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of ANN lies in its ability to represent both linear and non-linear relationships and in its ability to learn these relationships directly from the data being modelled. Modeling of rainfall runoff relationship is important in view of the many uses of water resources such as hydropower generation, irrigation, water supply and flood control. This study is to purposefully develop a rainfall runoff model for Sg. Tinjar with outlet at Long Jegan using Radial Basis Function (RBF) Neural Network. Training and simulation was done using Matlab 6.5.1 software with varying parameters to obtain the optimum result. Further, the results were compared to simulation done with Multilayer Percepteron model. The RBF network developed in this study has successfully modelled rainfall runoff relationship in Sungai Tinjar Catchment in Miri, Sarawak with an accuracy of about 98.3%.