Enhancing oceanic variables forecast in the Santos channel by estimating model error with Random Forests (2022)
- Authors:
- USP affiliated authors: DOTTORI, MARCELO - IO ; COZMAN, FABIO GAGLIARDI - EP ; COSTA, ANNA HELENA REALI - EP ; GOMI, EDSON SATOSHI - EP ; TANNURI, EDUARDO AOUN - EP ; FREITAS, LUCAS RODRIGUES DE - IB ; BARROS, MARCEL RODRIGUES DE - EP ; NETTO, CAIO FABRICIO DEBERALDINI - EP ; MATHIAS, MARLON SPROESSER - IEA ; MORENO, FELIPE MARINO - EP ; SCHIAVETO NETO, LUIZ ANDRÉ - EP
- Unidades: IO; EP; IB; IEA
- DOI: 10.48550/arXiv.2208.05966
- Subjects: FÍSICA ATMOSFÉRICA; FÍSICA DO ESTADO LÍQUIDO; APRENDIZADO COMPUTACIONAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: Cornell University Press
- Publisher place: Ithaca
- Date published: 2022
- Source:
- Título: [Proceedings]
- Conference titles: International Joint Conference on Artificial Intelligence - IJCAI 2022
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MORENO, Felipe Marino et al. Enhancing oceanic variables forecast in the Santos channel by estimating model error with Random Forests. 2022, Anais.. Ithaca: Cornell University Press, 2022. Disponível em: https://doi.org/10.48550/arXiv.2208.05966. Acesso em: 23 fev. 2026. -
APA
Moreno, F. M., Netto, C. F. D., Barros, M. R. de, Coelho, J. F., Freitas, L. P. de, Mathias, M. S., et al. (2022). Enhancing oceanic variables forecast in the Santos channel by estimating model error with Random Forests. In [Proceedings]. Ithaca: Cornell University Press. doi:10.48550/arXiv.2208.05966 -
NLM
Moreno FM, Netto CFD, Barros MR de, Coelho JF, Freitas LP de, Mathias MS, Schiaveto Neto LA, Dottori M, Cozman FG, Reali Costa AH, Gomi ES, Tannuri EA. Enhancing oceanic variables forecast in the Santos channel by estimating model error with Random Forests [Internet]. [Proceedings]. 2022 ;[citado 2026 fev. 23 ] Available from: https://doi.org/10.48550/arXiv.2208.05966 -
Vancouver
Moreno FM, Netto CFD, Barros MR de, Coelho JF, Freitas LP de, Mathias MS, Schiaveto Neto LA, Dottori M, Cozman FG, Reali Costa AH, Gomi ES, Tannuri EA. Enhancing oceanic variables forecast in the Santos channel by estimating model error with Random Forests [Internet]. [Proceedings]. 2022 ;[citado 2026 fev. 23 ] Available from: https://doi.org/10.48550/arXiv.2208.05966 - Modeling oceanic variables with dynamic graph neural networks
- Augmenting a physics-informed neural network for the 2D burgers equation by addition of solution data points
- A physics-informed neural network to model port channels
- Improving current forecast by leveraging measured data and numerical models via LiESNs
- A physics-informed neural operator for the simulation of surface waves
- Early detection of extreme storm tide events using multimodal data processing
- Automatic clustering of metocean conditions on the brazilian coast
- Azimuth stern drive (ASD) vector tugs positioning and towing force prediction during docking, steering and braking maneuvers
- Embracing data irregularities in multivariate time series with recurrent and graph neural networks
- Improving LLMs’ reasoning and planning with finite-state machines
Informações sobre o DOI: 10.48550/arXiv.2208.05966 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
Enhancing_oceanic_variabl... |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
