Using evolutionary multivariate adaptive regression splines approach to evaluate the eutrophication in the Pozón de la Dolores lake (Northern Spain)

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes when the productions of algae and other aquatic vegetations are increased, and deterioration of water quality and all its uses in general. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on multivariate adaptive regression splines (MARS) approach in combination with the artificial bee colony (ABC) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the MARS training procedure, which significantly influences the regression accuracy. 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 is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.85 for the Total phosphorus estimation and 0.84 for the Chlorophyll concentration were obtained when this hybrid ABC-MARS-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter. Finally, conclusions of this innovative research work are exposed.