Modelagem Chuva-vazão utilizando Redes Neurais Artificiais e Algoritmos Genéticos
Resumo
Este artigo apresenta uma metodologia para modelagem da relação de transformação chuva-vazão de uma bacia hidrográfica utilizando redes neurais artificiais (RNA) acopladas a um algoritmo genético (AG). Esta modelagem foi desenvolvida na bacia hidrográfica do rio Piquiri, localizada no estado do Paraná, Brasil. Para efeitos comparativos, foram desenvolvidas duas abordagens na modelagem: uma metodologia utilizando apenas uma RNA tipo MLP e outra usando uma RNA MLP com algoritmos genéticos. Observou-se que o modelo híbrido obteve melhor representação do processo de transformação chuva-vazão.
Referências
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Campolo, Marina and Andreussi, Paolo and Soldati, Alfredo. River flood forecasting with a neural network model. Water Resources Research, 35(4):1191-1197, 1999.
Cybenko, G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4):303-314, December 1989.
Dawson, Christian W. and Wilby, Robert. An artificial neural network approach to rainfall runoff modelling. Hydrological Science, (43):47-66, August 1998.
Haykin, Simon. Redes Neurais: Princípios e prática. Bookman, second edition, 2008.
Kwok-Wing; Cheng Chun-Tian e Qiu Lin Wang, Wen-Chuan; Chau. A comparison of performance of several arti_cial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3-4):294-306, August 2009.
Nasseri, M. and Asghari, K. and Abedini, M. Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3):1415-1421, October 2008.
Sedki, A. and Ouazar, D. and Elmazoudi, E. Evolving neural network using real coded genetic algorithm for daily rainfall runoff forecasting. Expert Systems with Applications, 36(3):4523-4527, April 2009.
Rezende, Solange Oliveira. Sistemas Inteligentes: Fundamentos e Aplicações. Ed. Manole, 2005.
Silva, Leandro Nunes de Castro. Análise e Síntese de Estratégias de Aprendizado para Redes Neurais Artificiais, Dissertação (Mestrado), Universidades Estadual de Campinas. 1998.
Tokar, A. Sezin and Johnson, Peggy A.. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 4(3):232-239, 1999.
Yao, Xin. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, Sept 1999.
Atiya, Amir F. e Shaheen, Samir I.. A Comparison Between Neural-Network Forecasting Techniques - Case Study: River Flow Forecasting. IEEE Transactions on Neural Networks, 10:402-409, mar 1999.
Back, T. and Hammel, U. and Schwefel H. P. Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1):317, April 1997.
Basheer, I.A. and Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43:3-31, dec 2000.
Campolo, Marina and Andreussi, Paolo and Soldati, Alfredo. River flood forecasting with a neural network model. Water Resources Research, 35(4):1191-1197, 1999.
Cybenko, G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4):303-314, December 1989.
Dawson, Christian W. and Wilby, Robert. An artificial neural network approach to rainfall runoff modelling. Hydrological Science, (43):47-66, August 1998.
Haykin, Simon. Redes Neurais: Princípios e prática. Bookman, second edition, 2008.
Kwok-Wing; Cheng Chun-Tian e Qiu Lin Wang, Wen-Chuan; Chau. A comparison of performance of several arti_cial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3-4):294-306, August 2009.
Nasseri, M. and Asghari, K. and Abedini, M. Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3):1415-1421, October 2008.
Sedki, A. and Ouazar, D. and Elmazoudi, E. Evolving neural network using real coded genetic algorithm for daily rainfall runoff forecasting. Expert Systems with Applications, 36(3):4523-4527, April 2009.
Rezende, Solange Oliveira. Sistemas Inteligentes: Fundamentos e Aplicações. Ed. Manole, 2005.
Silva, Leandro Nunes de Castro. Análise e Síntese de Estratégias de Aprendizado para Redes Neurais Artificiais, Dissertação (Mestrado), Universidades Estadual de Campinas. 1998.
Tokar, A. Sezin and Johnson, Peggy A.. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 4(3):232-239, 1999.
Yao, Xin. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, Sept 1999.
Publicado
19/07/2011
Como Citar
SCHEIDT, Felippe Alex; BRUNETTO, Maria Angelica de Camargo.
Modelagem Chuva-vazão utilizando Redes Neurais Artificiais e Algoritmos Genéticos. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 3. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1399-1408.
ISSN 2595-6124.
