Neural pattern recognition and multivariate data: water typology of the Paraı́ba do Sul River, Brazil

Modelling environmental processes is a complicated task, for the number of variables involved is usually high. This paper considers the use of neural pattern recognition to analyze structures within large data sets related to the study of ecological phenomena. The purpose is to use the information obtained with the aid of an unsupervised clustering step in a pattern recognition algorithm to obtain insight into the processes occurring along the period of observation. Once the processes are identified, more reliable models can be derived. The method proved to be helpful to highlight the major fluctuations in river water chemistry, and to identify complementary characteristics relevant to understanding the processes involved in the transport of dissolved nutrients in the Paraı́ba do Sul River basin outlet.