Rainfall-Runoff Modeling Using Artificial Neural Network

Abstract

The Artificial Neural Network (ANN) is a method of computation inspired by studies of the brain and nervous systems in biological organisms. A neural network method is considered as a robust tools for modelling many of complex non-linear hydrologic processes. It is a flexible mathematical structure which is capable of modelling the rainfall-runoff relationship due to its ability to generalize patterns in imprecise or ‘noisy’ and ambiguous input and output data sets. This paper describes the application of multilayer perceptron (MLP) and radial basis function (RBF) to predict daily runoff as a function of daily rainfall for the Sungai Lui, Sungai Klang, Sungai Bekok, Sungai Slim and Sungai Ketil catchments area. The performance of ANN is evaluated based on the efficiency and the error. It has been found that the ANN has a potential for successful application to the problem of runoff prediction.