Neural network models to forecast hydrological risk

Author

Cannas, B. ; Fanni, A. ; Pintus, M. ; Sechi, G.M.

Author_Institution

Dept. of Electr. & Electron. Eng., Cagliari Univ., Italy

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

423

Lastpage

426

Abstract

River flow forecasts are required to provide basic information for reservoir management in a multipurpose water system optimization framework. Moreover, an accurate short term prediction of flow rates is crucial for practical flood forecasting. In the paper, a neural approach is used to model the rainfall-runoff process in two different river sections in the same basin. Numerical results are provided for runoff prediction in the Tirso basin at the S. Chiara and Cantoniera sections in Sardinia (Italy), by considering hour and daily time steps

Keywords

feedforward neural nets; hydrology; multilayer perceptrons; rain; rivers; Cantoniera; Italy; S. Chiara; Sardinia; Tirso basin; flood forecasting; flow rates; hydrological risk; multipurpose water system optimization framework; neural network models; rainfall-runoff process; reservoir management; river flow forecasts; river sections; runoff prediction; short term prediction; Artificial neural networks; Floods; Hydrologic measurements; Hydrology; Mathematical model; Neural networks; Predictive models; Reservoirs; Rivers; Water resources;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005509

Filename

1005509