: In order to facilitate the monitoring of groundwater quality in France, the groundwater
bodies (GWB) in the Provence-Alpes-Côte d’Azur region have been grouped into 11 homogeneous
clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to
test the legitimacy of this grouping by predicting whether water samples belong to a given sampling
point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted
from the Size-Eaux database, and this dataset was processed using discriminant analysis and various
machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant
analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms
in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the
prediction was assessed using an approach combining recursive feature elimination (RFE) techniques
and random forest feature importance (RFFI). Major ions show high spatial range and play the main
role in discrimination, while trace elements and bacteriological parameters of high local and/or
temporal variability only play a minor role. The disparity of the results according to the characteristics
of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of
GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer,
homogeneous hydrogeological units, in order to optimize sustainable management of the resource by
the health agencies.