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Prediction of Phytoplankton Biomass in Small Rivers of Central Spain by Data Mining Method of Partial Least-Squares Regression

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Centro de Investigación y Desarrollo Tecnológico del Agua (CIDTA) Universidad de Salamanca, Campus Miguel de Unamuno Facultad de Farmacia s/n Salamanca, 37006 Salamanca, Spain
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Departament de Química Física; Universidad de Salamanca; Campus Miguel de Unamuno Facultad de Farmacia s/n Salamanca, 37006 Salamanca, Spain
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Instituto Politécnico Nacional, CIIDIR–Unidad Durango, Sigma 119, Fracc. 20 de Nov. II., 34220 Durango, México
*
Author to whom correspondence should be addressed.
Presented at the 4th International Electronic Conference on Water Sciences, 13–29 November 2019
Proceedings 2020, 48(1), 16; https://doi.org/10.3390/ECWS-4-06427
Published: 12 November 2019
(This article belongs to the Proceedings of The 4th International Electronic Conference on Water Sciences)
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
Keywords: phytoplankton; climate change; prediction; Partial Least-Squares Regression phytoplankton; climate change; prediction; Partial Least-Squares Regression
MDPI and ACS Style

García-Prieto, J.C.; Muñoz, F.J.B.; Roig, M.G.; Proal-Najera, J.B. Prediction of Phytoplankton Biomass in Small Rivers of Central Spain by Data Mining Method of Partial Least-Squares Regression. Proceedings 2020, 48, 16.

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