Abstract. A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing section in the hours preceding the event was used to characterize the behavior of the river system subject to the rainfall perturbation. Model predictions are very accurate (i.e., mean square error is less than 4%) when the model is used with a 1-hour time horizon. Increasing the time horizon, thus making the model suitable for flood forecasting, decreases the accuracy of the model. A limiting time horizon is found corresponding to the minimum time lag between the water level at the closing section and the rainfall, which is characteristic of each flooding event and depends on the rainfall and on the state of saturation of the basin. Performance of the model remains satisfactory up to 5 hours. A model of this type using just rainfall and water level information does not appear to be capable of predicting beyond this time limit.

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.Key words flood forecasting; artificial neural network; system response identification; nonlinear modelling; rainfall-runoff; River Arno, Italy Une approche à base de réseau de neurones artificiels pour la prévision des crues du fleuve Arno Résumé Le bassin du fleuve Arno est une zone sujette au phénomène des inondations, où le coût des dégâts dus aux inondations durant les 40 dernières années se chiffre à plus de 100 milliards d'euros. De nos jours, un événement aussi grave que l'inondation de 1966 à Florence produirait des dommages pour plus de 15.5 milliards d'euros. C'est pourquoi l'utilisation de systèmes de prévision et d'annonce précoce de crues s'avère nécessaire en vue d'une réduction des pertes économiques et du risque pour les personnes. Nous présentons dans ce travail un modèle de prévision de crues exploitant les informations en temps réel disponibles pour le bassin (données de précipitations, enregistrements hydrométriques et opérations de barrage), dans le but de prévoir l'évolution du niveau de l'eau. Le modèle est basé sur des réseaux de neurones artificiels qui ont été employés avec succès dans des travaux développés précédemment pour prévoir les crues dans un bassin non-régulé et pour prévoir Marina Campolo et al. 382l'évolution du niveau de l'eau dans le bassin de l'Arno en conditions d'écoulement réduit. Des prévisions précises sont obtenues à partir d'un jeu de données de deux ans et d'un traitement spécial des données ...

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Numerical simulations are used to characterize the fluid dynamic behavior of an industrialsize reactor. First, we focused on an experimental replica (∼440:1 volume ratio) of the full-scale reactor and evaluated the reliability of fully three-dimensional, time-dependent numerical computations of the flow field. An experiment was planned to obtain power data, which were compared with computer simulations for the scaled model, giving good agreement. Next, we examined the full-scale industrial reactor by exploiting the available macroscopic experimental observations and original computer simulations. We verified the scale-up of the two reactors by comparing the power number, discharge flow number, and pumping efficiency. Finally, by examining the power and stirring capability for different operating conditions, we found the operating conditions that ensured the optimum fluid dynamic efficiency.

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.