Abstract:Various types of neural networks have been proposed in previous papers for applications in hydrological events. However, most of these applied neural networks are classified as static neural networks, which are based on batch processes that update action only after the whole training data set has been presented. The time variate characteristics in hydrological processes have not been modelled well. In this paper, we present an alternative approach using an artificial neural network, termed real-time recurrent learning (RTRL) for stream-flow forecasting. To define the properties of the RTRL algorithm, we first compare the predictive ability of RTRL with least-square estimated autoregressive integrated moving average models on several synthetic time-series. Our results demonstrate that the RTRL network has a learning capacity with high efficiency and is an adequate model for time-series prediction. We also investigated the RTRL network by using the rainfall-runoff data of the Da-Chia River in Taiwan. The results show that RTRL can be applied with high accuracy to the study of real-time stream-flow forecasting networks.