Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Igua‡u River Basin

被引：13
作者：
Evsukoff, Alexandre Goncalves [1 ]
de Lima, Beatriz S. L. P. [1 ]
Ebecken, Nelson F. F. [1 ]
机构：
[1] Univ Fed Rio de Janeiro, COPPE, BR-21941972 Rio De Janeiro, Brazil
来源：
WATER RESOURCES MANAGEMENT | 2011年 / 25卷 / 03期
关键词：
Rainfall-runoff model; Catchments; Runoff; Recurrent fuzzy system; Data mining; NEURAL-NETWORKS; FUZZY; PREDICTION; FEEDFORWARD; SYSTEM;
D O I：
10.1007/s11269-010-9736-3
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
This work presents the development of a rainfall-runoff model for the Igua double dagger u River basin in southern Brazil. The model was developed to support the operation planning of hydroelectric power plants and is intended to predict the natural flow based on meteorological rain forecasts. A recurrent fuzzy system model was employed with parameters estimated by a genetic algorithm using observed rainfall as input. The model performs well using observed rainfall as input; however, its performance using predicted rainfall as input decays with the forecasting horizon, illustrating the effect of meteorological prediction errors. The prototype implementing the model has been used for dispatch planning by the Brazilian Electric System Operator.
引用
收藏
页码：963 / 985
页数：23
相关论文
共 26 条
[1]   Neural networks for real time catchment flow modeling and prediction [J].
Aqil, Muhammad ;
Kita, Ichiro ;
Yano, Akira ;
Nishiyama, Soichi .
WATER RESOURCES MANAGEMENT, 2007, 21 (10) :1781-1796
[2]  
Babuska R., 2003, Annual Reviews in Control, V27, P73, DOI 10.1016/S1367-5788(03)00009-9
[3]  
Bazartseren B, 2003, NEUROCOMPUTING, V55, P439, DOI [10.1016/S0925-2312(03)00388-6, 10.1016/S0925-2312(03)00388-5]
[4]   LEARNING LONG-TERM DEPENDENCIES WITH GRADIENT DESCENT IS DIFFICULT [J].
BENGIO, Y ;
SIMARD, P ;
FRASCONI, P .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (02) :157-166
[5]   Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling [J].
Chiang, YM ;
Chang, LC ;
Chang, FJ .
JOURNAL OF HYDROLOGY, 2004, 290 (3-4) :297-311
[6]  
CHOU SC, 2002, J GEOPHYS RES, V107, P537
[7]   Medium-range reservoir inflow predictions based on quantitative precipitation forecasts [J].
Collischonn, Walter ;
Morelli Tucci, Carlos Eduardo ;
Clarke, Robin Thomas ;
Chou, Sin Chan ;
Guilhon, Luiz Guilherme ;
Cataldi, Marcio ;
Allasia, Daniel .
JOURNAL OF HYDROLOGY, 2007, 344 (1-2) :112-122
[8]   Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model [J].
de Vos, N. J. ;
Rientjes, T. H. M. .
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2007, 52 (03) :397-413
[9]   A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam [J].
El-Shafie, Ahmed ;
Taha, Mahmoud Reda ;
Noureldin, Aboelmagd .
WATER RESOURCES MANAGEMENT, 2007, 21 (03) :533-556
[10]   Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors [J].
Evsukoff, A ;
Gentil, S .
ADVANCED ENGINEERING INFORMATICS, 2005, 19 (01) :55-66
123