Forecasting river flow rate during low-pow periods using neural networks

The pollution in the river Arno downstream of the city of Florence is a sev ere environmental problem during low-flow periods when the river flow rate is insufficient to support the natural waste assimilation mechanisms which include degradation, transport, and mixing. Forecasting the river flow rate during these low-flow periods is crucial for water quality management. In this paper a neural network model is presented for forecasting river flow f or up to 6 days. The model uses basin-averaged rainfall measurements, water level, and hydropower production data. It is necessary to use hydropower p roduction data since during low-flow periods the water discharged into the river from reservoirs can be a major fraction of total flow rate. Model pre dictions were found to be accurate with root-mean-square error on the predi cted river flow rate less then 8% over the entire time horizon of predictio n. This model will be useful for managing the water quality in the river wh en employed with river quality models.