@inproceedings{fdi:010089775,
  title = {{S}patial functionnal analysis application on fisheries acoustics data coupled with fine scale environmental data [r{\'e}sum{\'e} de poster]},
  author = {{K}ande, {Y}. and {D}abo-{N}iang, {S}. and {D}iogoul, {N}. and {B}rehmer, {P}atrice},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this work, we were interested in the application of functional, spatial data analysis ({FSDA}) on coupling acoustic ({S}v) and environmental (water temperature, fluorescence, salinity and turbid-ity) data. {T}o do this we use data from an acoustics fisheries surveys ({R}/{V} {T}halassa, {I}fremer, {AWA} campaign) carry out in {W}est {A}frican waters using multifrequency echosounder (18, 38, 70, 120, 333 k{H}z) and a scanfish (high performance towed undulator). {FSDA} were compared to classical statistical methods namely multivariate functional principal component analysis, classical prin-cipal component analysis, classification on principal component scores, classical additive model, spatial functional additive model. {T}he interest to improve such statistical analysis is applied here to the study the effect at fine scale of environmental parameters on the distribution of coastal sound scattered layers. {W}e first considered an aggregated analysis of the environmental data then we considered a more complete analysis of the data via their functional characters.},
  keywords = {{AFRIQUE} {DE} {L}'{OUEST} ; {ATLANTIQUE}},
  volume = {4},
  numero = {54},
  pages = {52},
  booktitle = {{W}orking group of fisheries acoustics, science and technology ({WGFAST})},
  year = {2022},
  ISSN = {2618-1371},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089775},
}