Artificial neural network approach to flood forecasting in the River Arno

Abstract The basin of the River Arno is a flood-prone area where flooding events have caused damage valued at more than 100 billion euro in the last 40 years. At present, the occurrence of an event similar to the 1966 flood of Firenze (Florence) would result in damage costing over 15.5 billion euro. Therefore, the use of flood forecasting and early warning systems is mandatory to reduce the economic losses and the risk for people. In this work, a flood forecasting model is presented that exploits the real-time information available for the basin (rainfall data, hydrometric data and information on dam operation) to predict the water-level evolution. The model is based on artificial neural networks, which were successfully used in previous works to predict floods in an unregulated basin and to predict water-level evolution in the Arno basin under low flow conditions. Accurate predictions are obtained using a two-year data set and a special treatment of input data; which allows a balance to be found between the spatial and temporal resolution of rainfall information and the model complexity. The prediction of water-level evolution remains accurate within a forecast time ahead of 6 h, which is the minimum time lag for the river to respond to dam releases under saturated conditions of the basin. The predicted flow rate percentage error ranges from 7 to 15% from the 1-h ahead to 6-h ahead predictions, and the accuracy of prediction increases for each time ahead of prediction, as the flow rate increases, suggesting that the model is particularly suited for flood forecasting purposes.