A multi-model approach for long-term runoff modeling using rainfall forecasts

被引：15
|
作者：
Evsukoff, Alexandre G. [1 ]
Cataldi, Marcio [2 ,3 ]
de Lima, Beatriz S. L. P. [1 ]
机构：
[1] Univ Fed Rio de Janeiro, COPPE, BR-21941972 Rio De Janeiro, RJ, Brazil
[2] Univ Fed Fluminense, Dept Engn Agr & Meio Ambiente, Niteroi, RJ, Brazil
[3] ONS, Rio De Janeiro, RJ, Brazil
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 05期
关键词：
Rainfall-runoff model; Catchments; Runoff; Recurrent fuzzy system; Data mining; NEURAL-NETWORKS; GENETIC ALGORITHM; FUZZY; PREDICTION; SCHEME; RIVER; FEEDFORWARD; SYSTEM;
D O I：
10.1016/j.eswa.2011.10.023
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work presents the development of a rainfall-runoff model for the lguacu River basin in the south of Brazil. The model was developed to support the operational planning of hydroelectric power plants and is intended to compute natural flow predictions based on meteorological rain forecasts. A recurrent fuzzy system model was employed, with parameters estimated by a genetic algorithm using observed rainfall as input. This work presents the recurrent fuzzy model within a multi-model approach, where the input data are furnished as an envelope, resulting in a prediction envelope that has demonstrated the ability to produce robust results. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：4938 / 4946
页数：9
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