Forecasting river flow rate during low‐flow periods using neural networks

Abstract

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.