Abstract
Data mining approach is applied to evaluate extreme rainfall events in the Brazil. Statistical analysis is combined with an artificial intelligence technique to identify the most relevant meteorological variables for a local severe precipitation in the Rio de Janeiro state (Brazil): Rio de Janeiro and Nova Friburgo cities. The p-value statistical technique is employed to select a much smaller subset of climatic variables, preserving the information associated with extreme meteorological events. A decision tree algorithm is used as a model to identify the precipitation severity. The method is tested with the events at Apr/2010 (Rio de Janeiro city) and at Jan/2011 (Nova Friburgo city). In both cases, our results show a good local analysis for extreme precipitation episodes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See the web-page: www.cptec.inpe.br/~rupload/arquivo/Notatec\RJ\060410.pdf~.
References
Freire, A., Lauriano, C., Araújo, G., Leta, T.: G1 RJ Número demortos na Região Serrana do Rio passa de 400. http://g1.globo.com/rio-de-janeiro/chuvas-no-rj/noticia/2011/01/numero-de-mortos-na-regiao-serrana-do-rio-passa-de-400.html. Accessed 20 Apr 2014
Hardin, J., Mitani, A., Hicks, L., VanKoten, B.: A robust measure of correlation between two genes on a microarray. BMC Bioinformatics 8, 220 (2007)
Moura, C.R.W., Escobar, G.C.J., Andrade, K.M.: Padrões de circulacão em superfície e altitude associados a eventos de chuva intensa na Região Metropolitana do Rio de Janeiro. Revista Brasileira de Meteorologia 28(3), 267–280 (2013)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Ruivo, H.M.: Metodologias de Mineracão de Dados em Análise Climática. Ph.D. Thesis, Applied Computing (CAP), National Institute for Space Research (INPE), São José dos Campo, São Paulo, Brazil (2013)
Ruivo, H.M., Sampaio, G., Ramos, F.M.: Knowledge extraction from large climatological data sets using a genome-wide analysis approach: application to the 2005 and 2010 Amazon droughts, pp. 1–15. Climatic Change (2014)
Ruivo, H.M., Campos Velho, H.F., Sampaio, G., Ramos, F.M.: Analysis of extreme precipitation events using a novel data mining approach. Am. J. Environ. Eng. 5, 96–105 (2015)
Simon, R.M., Korn, E.L., McShane, L.M., Radmacher, M.D., Wright, G.W., Zhao, Y.: Design and Analysis of DNA Microarray Investigations, vol. 209. Springer, New York (2003)
Witten, I.H., Frank, E.S.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann Publishers, San Mateo (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Ruivo, H.M., de Campos Velho, H.F., Freitas, S.R. (2017). Severe Precipitation in Brazil: Data Mining Approach. In: Constanda, C., Dalla Riva, M., Lamberti, P., Musolino, P. (eds) Integral Methods in Science and Engineering, Volume 2. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-59387-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-59387-6_22
Published:
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-319-59386-9
Online ISBN: 978-3-319-59387-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)