A Review on Artificial Neural Networks for Streamflow Prediction

Abstract

Stream flow prediction provides the information of various problems related to the design and effective operation of river balancing system. So it is an essentially important aspect of any watershed modelling. The black box models(ANN) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. This present study focusses on development of models using ANN for predicting the stream flow for Subarnarekha river basin. By reviewing the earlier research works, it is observed that the procedure addresses the selection of input variables, the definition of model architecture and the strategy of the learning process. The input vectors used for the models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow. Further, it is observed that the model performance was evaluated by statistical parameters like root-mean square error measures (RMSE), Nash-Sutcliffe efficiency (N-S) and squared correlation coefficient (R^2) and found that ANN model performance improved with increasing input vectors.