Neural Networks for River Flow Prediction

Abstract
The surface‐water hydrographs of rivers exhibit large variations due to many natural phenomena. One of the most commonly used approaches for interpolating and extending streamflow records is to fit observed data with an analytic power model. However, such analytic models may not adequately represent the flow process, because they are based on many simplifying assumptions about the natural phenomena that influence the river flow. This paper demonstrates how a neural network can be used as an adaptive model synthesizer as well as a predictor. Issues such as selecting an appropriate neural network architecture and a correct training algorithm as well as presenting data to neural networks are addressed using a constructive algorithm called the cascade‐correlation algorithm. The neural‐network approach is applied to the flow prediction of the Huron River at the Dexter sampling station, near Ann Arbor, Mich. Empirical comparisons are performed between the predictive capability of the neural network models and t...

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