Improving flood forecasting through feature selection by a genetic algorithm: experiments based on real data from an Amazon rainforest river (2021)
- Authors:
- Autor USP: UEYAMA, JO - ICMC
- Unidade: ICMC
- DOI: 10.1007/s12145-020-00528-8
- Subjects: ESTUDO DE CASO; ALGORITMOS GENÉTICOS; RIOS; REGRESSÃO LINEAR
- Keywords: River level forecasting; Feature selection
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2021
- Source:
- Título: Earth Science Informatics
- ISSN: 1865-0473
- Volume/Número/Paginação/Ano: v. 14, p. 37-50, 2021
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
VIEIRA, Alen Costa et al. Improving flood forecasting through feature selection by a genetic algorithm: experiments based on real data from an Amazon rainforest river. Earth Science Informatics, v. 14, p. 37-50, 2021Tradução . . Disponível em: https://doi.org/10.1007/s12145-020-00528-8. Acesso em: 22 mar. 2026. -
APA
Vieira, A. C., Garcia, G., Pabón, R. E. C., Cota, L. P., Souza, P. de, Ueyama, J., & Pessin, G. (2021). Improving flood forecasting through feature selection by a genetic algorithm: experiments based on real data from an Amazon rainforest river. Earth Science Informatics, 14, 37-50. doi:10.1007/s12145-020-00528-8 -
NLM
Vieira AC, Garcia G, Pabón REC, Cota LP, Souza P de, Ueyama J, Pessin G. Improving flood forecasting through feature selection by a genetic algorithm: experiments based on real data from an Amazon rainforest river [Internet]. Earth Science Informatics. 2021 ; 14 37-50.[citado 2026 mar. 22 ] Available from: https://doi.org/10.1007/s12145-020-00528-8 -
Vancouver
Vieira AC, Garcia G, Pabón REC, Cota LP, Souza P de, Ueyama J, Pessin G. Improving flood forecasting through feature selection by a genetic algorithm: experiments based on real data from an Amazon rainforest river [Internet]. Earth Science Informatics. 2021 ; 14 37-50.[citado 2026 mar. 22 ] Available from: https://doi.org/10.1007/s12145-020-00528-8 - NodePM: a remote monitoring alert system for energy consumption using probabilistic techniques
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