STREAMFLOW FORECASTING IN BUKIT MERAH WATERSHED BY USING ARIMA AND ANN

Abstract

This paper presents the application of linear and non-linear time series modeling approaches for simulating and forecasting streamflow at three stations located in three different rivers namely Kurau River, Ara River and Krian River of Bukit Merah watershed of Malaysia. The performance of linear autoregressive integrated moving average (ARIMA) model and non-linear artificial neural networks (ANN) model in forecasting the monthly streamflow of Malaysian river basins has been evaluated based on mean absolute percentage error (MAPE), root mean squared error (RMSE) and coefficient of determination (R²). The results show that both ARIMA and ANN methods are suitable for streamflow forecasting. However, ANN is better than ARIMA in dealing with short-memory streamflow data. In addition, ANN method is more flexible to use against the inconsistent data.

Keywords: time series, streamflow forecasting, ARIMA, ANN, Bukit Merah