A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study. (English) Zbl 1423.92006
Summary: The atypical proliferation of algae is a consequence of eutrophication, a phenomenon responsible for the deterioration of reservoirs and lakes. Its growth over the last few decades forced different administrations to adopt different solutions, including forecasting and management, with the help of mathematical models. This article presents a model of eutrophication of reservoirs based on a new methodology called multiscale Mexican Hat wavelet as the kernel function for the support vector regression (SVR) method and differential evolution (DE) optimization technique to estimate the abnormal proliferation of algae from physicochemical and biological variables. The present method implies the optimization of the SVR hyperparameters during the training process. In addition, five other SVR models with different nuclei (linear, quadratic, cubic, sigmoid and radial base function) and random forests (RF) were adjusted to experimental data for purposes of comparison. In addition to successfully predicting atypical algae growth (determination coefficients equal to 0.88 and 0.93), the model shown here can establish the importance of each biological and physicochemical parameter of improved algae growth. Finally, the main conclusions of this research work are presented.
Keywords:
support vector regression (SVR); wavelet kernel; differential evolution (DE); random forests (RF); eutrophication prediction in reservoirs; regression analysisSoftware:
LIBSVMReferences:
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