Modeling eutrophication and risk prevention in a reservoir in the Northwest of Spain by using multivariate adaptive regression splines analysis

The aim of this study was to obtain a predictive model able to perform an early detection of eutrophication using as predictors the chlorophyll concentration of the previous days. In this research work, the evolution of chlorophyll in the Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. For this purpose, some biological parameters (phytoplankton species expressed in biovolume) in addition to the most important physical-chemical parameters are considered. The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication in the reservoir is presented through the model. Secondly, a model for forecasting eutrophication 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.