SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Anderson Silva(achaves@lncc.br)
2 Thiago Moeda(tsantanna@on.br)
3 Fabio Porto (fporto@lncc.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Calvello, M., d’Orsi, R. N., Piciullo, L., Paes, N., Magalhaes, M., and Lacerda, W. A. (2015). The rio de janeiro early warning system for rainfall-induced landslides: analysis of performance for the years 2010–2013. International journal of disaster risk reduction, 12:3–15
2 de Souza, C. V. F., Barcellos, P. C. L., Crissaff, L., Cataldi, M., Mi- randa, F., and Lage, M. (2022). Visualizing simulation ensembles of extreme weather events.
3 e Souza, F. T., Ebecken, N., et al. (2012a). A data based model to predict landslide induced by rainfall in rio de janeiro city. Geotechnical and geological engineering, 30(1):85–94.
4 de Souza, F. T., Ebecken, N., et al. (2012b). A data based model to predict landslide induced by rainfall in rio de janeiro city. Geotechnical and geolog- ical engineering, 30(1):85–94
5 Farazmand, M. and Sapsis, T. P. (2019). Extreme events: Mechanisms and prediction. Applied Mechanics Reviews, 71(5)
6 O’Gorman, P. A. and Dwyer, J. G. (2018). Using ma- chine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events. Journal of Advances in Modeling Earth Systems, 10(10):2548–2563
7 Pereira, R. M. S., Wanderley, H. S., and Delgado, R. C. (2022). Ho- mogeneous regions for rainfall distribution in the city of rio de janeiro associated with the risk of natural disasters. Natural Hazards, 111(1):333–351.
8 Porto, F., Ferro, M., Ogasawara, E., Moeda, T., de Barros, C. D. T., Silva, A. C., Zorrilla, R., Pereira, R. S., Castro, R. N., Silva, J. V., et al. (2022). Ma- chine learning approaches to extreme weather events forecast in urban areas: Chal- lenges and initial results. Supercomputing Frontiers and Innovations, 9(1):49–73.
9 Qi, D. and Majda, A. J. (2020). Using machine learning to predict extreme events in complex systems. Proceedings of the National Academy of Sciences, 117(1):52–59
10 Sobral, B. S., de Oliveira-J ́unior, J. F., Alecrim, F., Gois, G., Muniz- J ́unior, J. G., de Bodas Terassi, P. M., Pereira-J ́unior, E. R., Lyra, G. B., and Zeri, M. (2020). Persiann-cdr based characterization and trend analysis of annual rainfall in rio de janeiro state, brazil. Atmospheric Research, 238:104873
11 Ummenhofer, C. C. and Meehl, G. A. (2017). Extreme weather and climate events with ecological relevance: a review. Philosophical Trans- actions of the Royal Society B: Biological Sciences, 372(1723):20160135
12 Brito, T. T., Oliveira-J ́unior, J. F., Lyra, G. B., Gois, G., and Zeri, M. (2017). Multivariate analysis applied to monthly rainfall over rio de janeiro state, brazil. Meteorology and Atmospheric Physics, 129(5):469–478.