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Campolo et al. 1999
Campolo, M., Soldati, A. and Andreussi, P. (1999). Forecasting river flow rate during low-flow periods using neural networks. Water Resources Research 35: doi: 10.1029/1999WR900205. issn: 0043-1397.

The pollution in the river Arno downstream of the city of Florence is a severe 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 for up to 6 days. The model uses basin-averaged rainfall measurements, water level, and hydropower production data. It is necessary to use hydropower production data since during low-flow periods the water discharged into the river from reservoirs can be a major fraction of total flow rate. Model predictions were found to be accurate with root-mean-square error on the predicted river flow rate less then 8% over the entire time horizon of prediction. This model will be useful for managing the water quality in the river when employed with river quality models. ¿ 1999 American Geophysical Union

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Abstract

Keywords
Hydrology, Surface water quality
Journal
Water Resources Research
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Publisher
American Geophysical Union
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