The determination of the processes responsible for the mineralization of groundwater
requires a large number of samples and physicochemical data, the treatment
of which proves to be tedious. Many statistical methods are developed to
facilitate the treatment of a certain volume of data. In this study, multivariate
statistical analysis including principal components analysis (PCA) and hierarchical
cluster analysis (HCA) have been applied on the groundwater physicochemical
data of the upper Kambo watershed. The aim is to simplify the data in
order to evaluate the relationships between the parameters and also to detect the
factors that influence the water chemistry. For the PCA, three factors were retained
representing 70% of the total inertia. Factor 1 shows the heterogeneity of
sources of acquisition of the mineralization by the positive correlation with all
the variables. Factor 2 indicates the dissolution of carbonates and anthropogenic
input. Factor 3 highlights the silicate, anthropogenic and atmospheric or marine
origin. The HCA enables to differentiate 4 groups of water according to the degree
of mineralization. Groups 1 and 2 represent the most mineralized waters,
dominated by calcium. The poor mineralized waters are groups 3 and 4 with the
rather close contents in calcium and sodium.