Artificial neural network model for synthetic streamflow generation

Time series of streamflow plays an important role in planning, design and management of water resources system. In the event of non availability of a long series of historical streamflow record, generation of the data series is of utmost importance. Although a number of models exist, they may not always produce satisfactory result in respect of statistics of the historical data. In such event, artificial neural network (ANN) model can be a potential alternative to the conventional models. Streamflow series, which is a stochastic phenomenon, can be suitably modeled by ANN for its strong capability to perform non-linear mapping. An ANN model developed for generating synthetic streamflow series of the Pagladia River, a major north bank tributary of the river Brahmaputra, is presented in this paper along with its comparison with other existing models. The comparison carried out in respect of five different statistics of the historical data and synthetically generated data has shown that among the different models, viz., autoregressive moving average (ARMA) model, Thomas-Fiering model and ANN model, the ANN based model has performed better in generating synthetic streamflow series for the Pagladia River. Copyright Springer Science + Business Media B.V. 2007