Neural Network Model for Prediction of Discharged from the Catchments of Langat River, Malaysia

Abstract

Artificial neural networks have been shown to be able to approximate any continuous non-linear functions and have been used to build data base empirical models for non-linear processes. In this study, neural networks models were used to model the daily river flows or discharged in Langat River, Malaysia. Two possible ways of modelling were implemented which is by time series prediction and by the dynamics function of the system which include the past value of the discharged and also the rainfall in the input. The sum square error (SSE), residue analysis and correlation coefficient based on the observed and prediction output is chosen as the criteria of selection of which models is appropriate. It was found that the developed neural networks models using dynamics function provided satisfactory model discharges.