LAUSR

Abstract Algal atypical proliferation is a consequence of water fertilization (also called eutrophication) and one of the main causes of the degradation of reservoir and lake ecosystems. Its intensification during the last decades has led the stakeholders to seek water management and restoration solutions, including those based on modelling approaches. In this way, this paper presents one reservoir eutrophication modelling based on a new hybrid algorithm that combines multivariate adaptive regression splines (MARS) and differential evolution (DE) to estimate the algal abnormal proliferation from physical-chemical and biological variables. This technique involves the optimization of the MARS hyperparameters during the training process. Additionally, an M5 model tree was fitted to the experimental data for comparison purposes. Apart from successfully forecasting algal atypical growth (coefficients of determination equal to 0.83 and 0.91), the model showed here can establish the significance of each biological and physical-chemical parameter of the algal enhanced growth. Finally, the main conclusions of this research work are exposed.