Uso de Redes Neurais para Previsão da Temperatura da Superfície do Mar do Oceano Atlântico Tropical
Resumo
Eventos ambientais extremos afetam a vida de milhões de pessoas. As secas encontram-se nesta categoria. Embora não se possa impedir que este tipo de evento ocorra, a previsão de sua ocorrência possibilita mitigar parte do dano resultante. Uma das variáveis importantes para identificação de ocorrências de secas é a temperatura de superfície do mar (TSM). Este trabalho apresenta um modelo baseado em Redes Neurais para previsão de TSM do Oceano Atlântico Tropical. O modelo é constituído por dois workflows. O primeiro workflow realiza o ajuste do modelo de previsão, enquanto que o segundo realiza a previsão propriamente dita. Os experimentos preliminares apresentaram resultados positivos e apontam para oportunidades de refinamento de modelos tanto para horizontes de previsão de curto prazo como de longo prazo.
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Referências
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De Gooijer, J. G. and Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3):443–473.
Diebold, F. X. and Mariano, R. S. (2002). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 20(1):134–144.
EM-DAT (2015). The OFDA/CRED International Disaster Data Base. Centre for Research on the Epidemiology of Disasters. Technical report, http://www.emdat.be/database.
Good, P., Lowe, J. A., and Rowell, D. P. (2008). Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate. Climate Dynamics, 32(2-3):205–218.
GOOS-Brasil (2015). PIRATA Dataset. Technical report, http://www.goosbrasil.org.
Hastenrath, S. (2011). Exploring the climate problems of Brazil’s Nordeste: a review. Climatic Change, 112(2):243–251.
Lins, I. D., Araujo, M., Moura, M. d. C., Silva, M. A., and Droguett, E. L. (2013). Prediction of sea surface temperature in the tropical Atlantic by support vector machines. Computational Statistics & Data Analysis, 61:187–198.
Marinho, A., Murta, L.,Werner, C., Braganholo, V., Cruz, S. M. S. d., Ogasawara, E., and Mattoso, M. (2012). ProvManager: a provenance management system for scientific workflows. Concurrency and Computation: Practice and Experience, 24(13):1513–1530.
Mishra, A. K. and Singh, V. P. (2011). Drought modeling – A review. Journal of Hydrology, 403(1–2):157–175.
Ogasawara, E., Dias, J., Silva, V., Chirigati, F., de Oliveira, D., Porto, F., Valduriez, P., and Mattoso, M. (2013). Chiron: a parallel engine for algebraic scientific workflows. Concurrency and Computation: Practice and Experience, 25(16):2327–2341. 00015.
Parisi, F., Sliva, A., and Subrahmanian, V. S. (2013). A temporal database forecasting algebra. International Journal of Approximate Reasoning, 54(7):827–860.
Shumway, R. H. and Stoffer, D. S. (2010). Time Series Analysis and Its Applications: With R Examples. Springer, New York, 3rd ed. 2011 edition edition. 01259.
Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J., Rippey, B., Tinker, R., Palecki, M., Stooksbury, D., Miskus, D., and Stephens, S. (2002). The Drought Monitor. Bulletin of the American Meteorological Society, 83(8):1181–1190.
Bourlés, B., Lumpkin, R., McPhaden, M. J., Hernandez, F., Nobre, P., Campos, E., Yu, L., Planton, S., Busalacchi, A., Moura, A. D., Servain, J., and Trotte, J. (2008). The Pirata Program: History, Accomplishments, and Future Directions. Bulletin of the American Meteorological Society, 89(8):1111–1125.
De Gooijer, J. G. and Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3):443–473.
Diebold, F. X. and Mariano, R. S. (2002). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 20(1):134–144.
EM-DAT (2015). The OFDA/CRED International Disaster Data Base. Centre for Research on the Epidemiology of Disasters. Technical report, http://www.emdat.be/database.
Good, P., Lowe, J. A., and Rowell, D. P. (2008). Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate. Climate Dynamics, 32(2-3):205–218.
GOOS-Brasil (2015). PIRATA Dataset. Technical report, http://www.goosbrasil.org.
Hastenrath, S. (2011). Exploring the climate problems of Brazil’s Nordeste: a review. Climatic Change, 112(2):243–251.
Lins, I. D., Araujo, M., Moura, M. d. C., Silva, M. A., and Droguett, E. L. (2013). Prediction of sea surface temperature in the tropical Atlantic by support vector machines. Computational Statistics & Data Analysis, 61:187–198.
Marinho, A., Murta, L.,Werner, C., Braganholo, V., Cruz, S. M. S. d., Ogasawara, E., and Mattoso, M. (2012). ProvManager: a provenance management system for scientific workflows. Concurrency and Computation: Practice and Experience, 24(13):1513–1530.
Mishra, A. K. and Singh, V. P. (2011). Drought modeling – A review. Journal of Hydrology, 403(1–2):157–175.
Ogasawara, E., Dias, J., Silva, V., Chirigati, F., de Oliveira, D., Porto, F., Valduriez, P., and Mattoso, M. (2013). Chiron: a parallel engine for algebraic scientific workflows. Concurrency and Computation: Practice and Experience, 25(16):2327–2341. 00015.
Parisi, F., Sliva, A., and Subrahmanian, V. S. (2013). A temporal database forecasting algebra. International Journal of Approximate Reasoning, 54(7):827–860.
Shumway, R. H. and Stoffer, D. S. (2010). Time Series Analysis and Its Applications: With R Examples. Springer, New York, 3rd ed. 2011 edition edition. 01259.
Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J., Rippey, B., Tinker, R., Palecki, M., Stooksbury, D., Miskus, D., and Stephens, S. (2002). The Drought Monitor. Bulletin of the American Meteorological Society, 83(8):1181–1190.
Publicado
26/08/2015
Como Citar
MATTOS, Patrícia; IORGULESCU, Ana Maria; SALLES, Rebecca; BEZERRA, Eduardo; OGASAWARA, Eduardo; LIMA, Leonardo.
Uso de Redes Neurais para Previsão da Temperatura da Superfície do Mar do Oceano Atlântico Tropical. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 9. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 131-140.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2015.7214.
