Marina Campolo

River flood forecasting with a neural network model

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.

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.

FORECASTING RIVER FLOW RATE DURING LOW-FLOW PERIODS USING NEURAL NETWORKS

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.

Water quality control in the river Arno

In this work, we analyzed pollution in the river Arno using a non-steady advection-dispersion-reaction equation (ADRE) calibrated on experimental data. We examined the influence different pollution control strategies have on dissolved oxygen (DO). We considered (i) flow rate variation; (ii) local oxygenation at critical points; (iii) dynamic modification of wastewater load. Results indicate first, that reservoir management is effective in reducing pollution; second, that local oxygenation is necessary to ensure that DO does not fall below safety levels; and finally, that tuning wastewater loads appears to be impractical to manage the river quality given the stringent limitations it would impose on the industrial effluents.

Time-dependent flow structures and Lagrangian mixing in Rushton-impeller baffled-tank reactor

The object of this work is to investigate the role of large-scale convective structures in promoting mixing in a stirred tank. We focus on a standard geometry (

Time-dependent finite-volume simulation of the turbulent flow in a free-surface CSTR

at bottom, four-ba*e reactor stirred by a six-blade Rusthon impeller) and we use an Eulerian–Lagrangian approach to investigate numerically the dispersion of

Turbulent Drag Reduction by Biopolymers in Large Scale Pipes

uid particles. The three-dimensional, time-dependent, fully developed

Protocols to compare infusion distribution of wound catheters

ow 8eld is calculated with a computationally e9cient procedure using a RANS solver with k–� turbulence modeling and the

Aerodynamic Analysis of a Two-Man Bobsleigh

ow 8eld is assessed precisely against experimental data. Then,

Influence of Jet Inlet Conditions on Time-Average Behavior of Transverse Jets

uid parcels are tracked in the calculated