A two-step-ahead recurrent neural network for stream-flow forecasting

In many engineering problems, such as flood warning systems, accurate multistep-ahead prediction is critically important. The main purpose of this study was to derive an algorithm for two-step-ahead forecasting based on a real-time recurrent learning (RTRL) neural network that has been demonstrated as best suited for real-time application in various problems. To evaluate the properties of the developed two-step-ahead RTRL algorithm, we first compared its predictive ability with least-square estimated autoregressive moving average with exogenous inputs (ARMAX) models on several synthetic time-series. Our results demonstrate that the developed two-step-ahead RTRL network has efficient ability to learn and has comparable accuracy for time-series prediction as the refitted ARMAX models. We then investigated the two-step-ahead RTRL network by using the rainfall-runoff data of the Da-Chia River in Taiwan. The results show that the developed algorithm can be successfully applied with high accuracy for two-step-ahead real-time stream-flow forecasting. Copyright (C) 2003 John Wiley Sons, Ltd.