@article{fdi:010094272,
  title = {{N}utrient estimation in the {P}eruvian upwelling system based on a neural network approach},
  author = {{A}sto, {C}. and {B}osse, {A}. and {P}ietri, {A}lice and {S}auz{\`e}de, {R}. and {G}raco, {M}. and {G}uti{\'e}rrez, {D}. and {C}olas, {F}ran{\c{c}}ois},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study presents a regionally trained version of the "{CA}rbonate system and {N}utrients concentration from h{Y}drological properties and {O}xygen using a {N}eural network" ({CANYON}) method, named {CANYON}-{PU}, for estimating primary macronutrients (phosphates, silicates, and nitrates) in the {P}eruvian {U}pwelling {S}ystem ({PUS}). {U}sing a neural network approach, the model was trained using extensive biogeochemical data spanning between 2003 and 2021, collected by the {P}eruvian {I}nstitute of {M}arine {R}esearch ({IMARPE}). {V}ariables representing the low-frequency variability related to {ENSO} were introduced in the training and significantly improved the performance of the algorithm. {T}he performance of {CANYON}-{PU} was validated against independent datasets and demonstrated an improvement in accuracy over the global {CANYON} model that struggled to represent the nutrient distribution in the {PUS} mainly due to the lack of samples in its training. {T}herefore, {CANYON}-{PU} successfully captured nutrient variability across different spatial and temporal scales, showcasing its applicability to diverse datasets, including high-frequency data such as profiling floats or gliders. {T}his work highlights the effectiveness of neural networks for representing the nutrient distribution within highly variable ecosystems like the {PUS}.},
  keywords = {{P}eruvian upwelling system ; nutrients ; neural network ; {E}l {N}iño ; gliders ; profiling float ; {PEROU} ; {PACIFIQUE}},
  booktitle = {},
  journal = {{F}rontiers in {M}arine {S}cience},
  volume = {12},
  numero = {},
  pages = {1558747 [18 ]},
  year = {2025},
  DOI = {10.3389/fmars.2025.1558747},
  URL = {https://www.documentation.ird.fr/hor/fdi:010094272},
}