@article{fdi:010086831,
  title = {{D}ata conditioning modes for the study of groundwater resource quality using a large physico-chemical and bacteriological database, {O}ccitanie region, {F}rance},
  author = {{J}abrane, {M}. and {T}ouiouine, {A}. and {B}ouabdli, {A}. and {C}hakiri, {S}. and {M}ohsine, {I}. and {V}alles, {V}. and {B}arbi{\'e}ro, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{W}hen studying large multiparametric databases with very heterogeneous parameters (microbiological, chemical, and physicochemical), covering a wide and heterogeneous area, the probability of observing extreme values ({Z}-score > 2.5) is high. {T}he information carried by these few samples monopolizes a large part of the information conveyed by the entire database. {T}he study of the spatial structure of the data and the identification of the mechanisms responsible for the water quality are then strongly degraded. {D}ata transformation can be proposed to overcome these problems. {T}his study deals with a database of 8110 groundwater analyses ({O}ccitanie region, {F}rance), on which the bacteriological load was measured in {E}scherichia coli and {E}nterococci, in addition to electrical conductivity, major ions, {M}n, {F}e, {A}s and p{H}. {T}hree modes of data conditioning were tested and compared to the treatment with raw data. {T}he results show that log transformation is the best option, revealing a relationship between {E}. coli content and all the other parameters. {B}y reducing the impact of extreme values without eliminating them, it allowed a concentration of information on the first factorial axes of the {PCA}, and consequently a better definition of the associated processes. {T}he spatial structure of the principal components and their cartographic representation is improved. {T}he conditioning of the data with the square root function led to an intermediate improvement between the logarithmic transformation and the absence of conditioning. {T}he application of these results should allow a targeted, more efficient, and therefore, less expensive monitoring of water quality by {R}egional {H}ealth {A}gencies.},
  keywords = {groundwater resource ; groundwater management ; large database ; log-transformation ; {O}ccitanie ; {F}rance ; {FRANCE}},
  booktitle = {},
  journal = {{W}ater},
  volume = {15},
  numero = {1},
  pages = {84 [16 p.]},
  year = {2023},
  DOI = {10.3390/w15010084},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086831},
}