Improving current forecast by leveraging measured data and numerical models via LiESNs (2025)
- Authors:
- USP affiliated authors: CORREIA, ARTUR JORDÃO LIMA - EP ; MATHIAS, MARLON SPROESSER - EP ; DOTTORI, MARCELO - IO ; COSTA, ANNA HELENA REALI - EP ; GOMI, EDSON SATOSHI - EP ; COZMAN, FABIO GAGLIARDI - EP ; TANNURI, EDUARDO AOUN - EP ; MORENO, FELIPE MARINO - EP ; BARROS, MARCEL RODRIGUES DE - EP ; Mathias, Marlon Sproesser - EP
- Unidades: EP; IO
- DOI: 10.1016/j.envsoft.2025.106556
- Subjects: APRENDIZADO COMPUTACIONAL; REDES NEURAIS; PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
- Keywords: REDE ECHO STATE
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Environmental Modelling and Software
- ISSN: 1364-8152
- Volume/Número/Paginação/Ano: v. 192, p. 1-13, article nº 106556, 2025
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
MORENO, Felipe Marino et al. Improving current forecast by leveraging measured data and numerical models via LiESNs. Environmental Modelling and Software, v. 192, p. 1-13, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.envsoft.2025.106556. Acesso em: 10 abr. 2026. -
APA
Moreno, F. M., Barros, M. R. de, Correia, A. J. L., Mathias, M. S., Dottori, M., Reali Costa, A. H., et al. (2025). Improving current forecast by leveraging measured data and numerical models via LiESNs. Environmental Modelling and Software, 192, 1-13. doi:10.1016/j.envsoft.2025.106556 -
NLM
Moreno FM, Barros MR de, Correia AJL, Mathias MS, Dottori M, Reali Costa AH, Gomi ES, Cozman FG, Tannuri EA. Improving current forecast by leveraging measured data and numerical models via LiESNs [Internet]. Environmental Modelling and Software. 2025 ; 192 1-13.[citado 2026 abr. 10 ] Available from: https://doi.org/10.1016/j.envsoft.2025.106556 -
Vancouver
Moreno FM, Barros MR de, Correia AJL, Mathias MS, Dottori M, Reali Costa AH, Gomi ES, Cozman FG, Tannuri EA. Improving current forecast by leveraging measured data and numerical models via LiESNs [Internet]. Environmental Modelling and Software. 2025 ; 192 1-13.[citado 2026 abr. 10 ] Available from: https://doi.org/10.1016/j.envsoft.2025.106556 - A physics-informed neural network to model port channels
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