Prediction of Phytoplankton Biomass in Small Rivers of Central Spain by Data Mining Method of Partial Least-Squares Regression
The Water Framework Directive (WFD, EC, 2000) states that the “good” ecological status of natural water bodies must be based on their chemical, hydromorphological and biological features, especially under drastic conditions of floods or droughts. Phytoplankton is considered a good environmental bioindicator (WFD) and climate change has a strong impact on phytoplankton communities and water quality. The development of robust techniques to predict and control phytoplankton growth is still in progress. The aim of this study is to analyze the impact of the different stressors associated with the change in phytoplanktonic communities in small rivers in the center of the Iberian Peninsula (Southwestern Europe). A statistical study on the identification of the essential limiting variables in the phytoplankton growth and its seasonal variation by climate change was carried out. In this study, a new method based on the partial least-squares (PLS) regression technique has been used to predict the concentration of phytoplankton and cyanophytes from 22 variables usually monitored in rivers. The predictive models have shown a good agreement between training and test data sets in rivers and seasons (dry and wet). The phytoplankton in dry periods showed greatest similarities, these dry periods being the most important factor in the phytoplankton proliferation