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- Title
Modelling algal abnormal proliferation in a reservoir using support vector regression: a case study.
- Authors
Díaz Muñiz, C.; Alonso Fernández, J. R.; García Nieto, P. J.; Alvarez Antón, J. C.
- Abstract
An important issue in lake and reservoir management is to prevent algal proliferation and its problematic symptom: the algal blooms that deteriorate water quality and ruin its use. The aim of this study was to obtain a predictive model able to perform an early detection of algal abnormal proliferation using as a predictor the chlorophyll a (Chl a) concentration of previous days. For this purpose, some biological parameters (phytoplankton species expressed in biovolume) in addition to the most important chemical and physical-chemical parameters were considered. The implementation of the statistical learning method known as support vector machines (SVMs) allowed us to predict the Chl a concentration, one of the most frequently used estimators of algal biomass, and to study its evolution in the Trasona reservoir (Principality of Asturias, Northern Spain). In the first place, the main goal was to obtain the dependence relationship of chlorophyll concentration as a function of not only several chemical and physical-chemical variables but also the biological ones. This dependence relationship was useful in setting the significance order of the variables involved in the Chl a prediction. Secondly, a model for forecasting the algal abnormal proliferation is obtained. The agreement between experimental data and the model confirmed the good performance of the latter. Finally, conclusions of this innovative research work are exposed. Copyright © 2014 John Wiley & Sons, Ltd.
- Subjects
ALGAL blooms; RESERVOIRS; LAKE management; SUPPORT vector machines; WATER quality
- Publication
Ecohydrology, 2015, Vol 8, Issue 6, p1109
- ISSN
1936-0584
- Publication type
Article
- DOI
10.1002/eco.1568