Proposta de um Método para Previsão de Cheias Sazonais Utilizando Redes Neurais Artificiais: Uma Aplicação ao Rio Amazonas
Abstract
This paper proposes a new method for forecasting the maximum seasonal amplitude of rivers, using feedforward neural networks and, as input variables, climatic indices and the river amplitude measured a few months earlier before the maximum amplitude be verified. A new method for selecting the most relevant prediction variables is proposed. For neural networks training, two methods for improving its generalization are used: early stop and regularization. The best prediction result is obtained with two input variables, resulting in a correlation prediction coefficient of rp = 0,755.
References
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Vörösmarty, C.J., Fekete, B. and Tucker B.A. (1998). River Discharge Database, Version 1.1 (RivDIS v1.0 supplement). Institute for the Study of Earth, Oceans, and Space / University of New Hampshire, Durham NH (USA). http://www.rivdis.sr.unh.edu/.
Coe, M. T., Costa, M. H., Botta, A. and Birkett, C. (2002) Long-term simulations of discharge and floods in the Amazon Basin. In Journal of Geophysical Research. art. no. 8044.
Demuth H; Beale M; Hagan M. Neural Network Toolbox User's Guide. Available from:http://www.mathworks.com/products/neural-network/description6.html. Acessado em 24/02/2015.
Doan, C.D. and Liong, S.Y. (2004). Generalization for multilayer neural network Bayesian regularization or early stopping. In: Proceedings of 2nd Conference of Asia Pacific Association of Hydrology and Water Resources. pages 1-8.
Junk, W.J., Ohly, J.J., Piedade, M.T.F. and Soares, M.G.M. (2000) The Central Amazon Floodplain: Actual Use and Options for a Sustainable Management. Backhuys Publishers B.V, Leiden.
Junk W. J., Furch, K. (1985). The physical setting and chemical properties of Amazonian waters and their relationships with the biota. In Amazonia, Prance GT, Lovejoy TE(eds). Pergamon Press: Oxford
Porto de Manaus, http://www.portodemanaus.com.br. Acessado em 23/02/2015.
Russell, G., Miller, J. (1990). Global river runoff calculated from a global atmospheric general circulation model. In Journal of Hydrology, v.117, pages 241-254.
Schongart, J and Junk, W. J. (2007). Forecasting the flood-pulse in Central Amazonia by ENSO-indices. In: Journal of Hydrology, v. 335, pages 124–132.
Sonka, M and Fizpratick, J. M. (2000) Handbook of Medical Imaging. Volume 2: Medical Imaging Processing and Analysis. Washington: Spie Press.
Theodoridis,S., Koutroumbas, K. (2006) Pattern Recognition, Elsevier Academic Press, San Diego.
Trenberth, K.E. and Stepaniak, D.P. (2001). Indices of El Niño evolution. In Journal of Climate. v.14, pages. 1697–1701.
Uvo, C. B., Tolle, U. and Berndtsson, R. (2000). Forecasting Discharge in Amazonia Using Artificial Neural Networks. In: International Journal of Climatology, v.20. pages 1495 – 1507.
Vörösmarty, C.J., Fekete, B. and Tucker B.A. (1998). River Discharge Database, Version 1.1 (RivDIS v1.0 supplement). Institute for the Study of Earth, Oceans, and Space / University of New Hampshire, Durham NH (USA). http://www.rivdis.sr.unh.edu/.
Published
2015-07-20
How to Cite
RODRIGUES, Márcio; COSTA, Marly; C. FILHO, Cícero.
Proposta de um Método para Previsão de Cheias Sazonais Utilizando Redes Neurais Artificiais: Uma Aplicação ao Rio Amazonas. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 6. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 1-10.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2015.10184.
