Pattern Recognition Techniques for the Evaluation of Spatial and Temporal Variations in Water Quality. A Case Study: Suquía River Basin (Córdoba-Argentina).

WUNDERLIN, DANIEL; DIAZ, MARÍA DEL PILAR; AMÉ, MARÍA VALERIA; PESCE, SILVIA; HUED, ANDREA; BISTONI, MARÍA DE LOS ANGELES

Elsevier

Lugar: Copenhague, Dinamarca; Año: 2001 vol. 35 p. 2881 - 2881

font color="#0000ff" size="1" face="Arial">We report a comparative study using three different chemometric techniques to evaluate both spatial and temporal changes in Suquía River water quality, with a special emphasis on the improvement obtained using discriminant analysis for such evaluation. We have monitored 22 parameters at different stations from the upper, middle, and beginning of the lower river basin during at least two years including 232 different samples. We obtained a complex data matrix, which was treated using the pattern recognition techniques of cluster analysis (CA), factor analysis/principal components (FA/PCA), and discriminant analysis (DA). CA renders good results as a first exploratory method to evaluate both spatial and temporal differences, however it fails to show details of these differences. FA/PCA needs 13 parameters to point out 71% of both temporal and spatial changes; consequently data reduction from FA/PCA in this case is not as considerable as expecte