Neural network models to forecast hydrological risk

被引：3

作者：

Cannas, B ^{[1
]}

Fanni, A ^{[1
]}

Pintus, M ^{[1
]}

Sechi, GM ^{[1
]}

机构：

[1] Univ Cagliari, Dept Elect & Elect Engn, Cagliari, Italy

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

D O I：

10.1109/IJCNN.2002.1005509

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 this 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.

引用

页码：423 / 426

页数：4

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