Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil (2016)
- Authors:
- USP affiliated authors: MENDIONDO, EDUARDO MARIO - EESC ; UEYAMA, JO - ICMC
- Unidades: EESC; ICMC
- DOI: 10.1007/s00521-015-1930-z
- Subjects: SISTEMAS DISTRIBUÍDOS; PROGRAMAÇÃO CONCORRENTE
- Keywords: Wireless sensor network
- Language: Inglês
- Imprenta:
- Source:
- Título: Neural Computing and Applications
- ISSN: 0941-0643
- Volume/Número/Paginação/Ano: v. 27, p. 1129-1141, 2016
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
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ABNT
FURQUIM, Gustavo et al. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, v. 27, p. 1129-1141, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1930-z. Acesso em: 28 dez. 2025. -
APA
Furquim, G., Pessin, G., Faiçal, B. S., Mendiondo, E. M., & Ueyama, J. (2016). Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, 27, 1129-1141. doi:10.1007/s00521-015-1930-z -
NLM
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1007/s00521-015-1930-z -
Vancouver
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1007/s00521-015-1930-z - A middleware platform to support river monitoring using wireless sensor networks
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- Alerta contra inundações
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- An accurate flood forecasting model using wireless sensor networks and chaos theory: a case study with real WSN deployment in Brazil
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- AGORA-GeoDash: a geosensor dashboard for real-time flood risk monitoring
- A distributed approach to flood prediction using a WSN and ML: a comparative study of ML techniques in a WSN deployed in Brazil
Informações sobre o DOI: 10.1007/s00521-015-1930-z (Fonte: oaDOI API)
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