A sequential approach for short-term water level prediction using nonlinear autoregressive neural networks

Abstract

The water level in an artificial lake is important not only for the production of electric energy but also for other activities such as tourism, irrigation and drought control. The water level in the lake is influenced by various factors, among which the most important include: the water inflow, discharge of water and water seepage. In this research, artificial neural networks (ANN) are selected for the water level prediction because of their well-known abilities for learning from examples. A total of 29 years of water level measurement data was used for ANN training and validation. This paper represents a sequential approach for the short-term water level prediction in Jablanicko lake by using only water level data. With regard to sequential approach for every step of the prediction, the most recent data were used for ANN training. Two types of ANNs were used in this study: Nonlinear Autoregressive (NAR) neural networks and Feed Forward Back Propagation (FFBP) neural networks. The main focus of this study was on NAR networks prediction of water level, while FFBP networks were used for comparison purposes. The results showed that neural networks can provide quality water level prediction even if only water level data is used.