Modeling algal atypical proliferation in La Barca reservoir using L-SHADE optimized gradient boosted regression trees: a case study | Neural Computing and Applications
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Modeling algal atypical proliferation in La Barca reservoir using L-SHADE optimized gradient boosted regression trees: a case study

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Abstract

Algal atypical proliferation is a consequence of water fertilization (also called eutrophication) and a worldwide environmental concern since water quality and its uses are seriously compromised. Prevention is the most effective measure given that once the algal proliferation starts, it is too difficult and costly to stop the process. This article presents a nonparametric machine learning algorithm that combines the gradient boosted regression tree (GBRT) model and an improved differential evolution algorithm (L-SHADE) for better understanding and control of the algal abnormal proliferation (usually estimated from Chlorophyll-a and Total Phosphorus concentrations) from physicochemical and biological variable values obtained in a northern Spain reservoir. This L-SHADE technique involves the optimization of the GBRT hyperparameters during the training process. Apart from successfully estimating algal atypical growth (coefficients of determination equal to 0.91 and 0.93 for Chlorophyll-a and Total Phosphorus concentrations were obtained, respectively), this hybrid model allows here to establish the ranking of each independent biological and physicochemical variable according to its importance in the algal enhanced growth.

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Acknowledgements

The authors wish to thank the Cantabrian Basin Authority (Spanish Ministry of Agriculture, Food and Environment) for providing the experimental dataset used in this research. Additionally, we would like to thank Anthony Ashworth for his revision of English grammar and spelling of the manuscript.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007, Oviedo, Spain

    Paulino José García-Nieto & Esperanza García-Gonzalo

  2. Cantabrian Basin Authority, Spanish Ministry for the Ecological Transition and Demographic Challenge, 33071, Oviedo, Spain

    José Ramón Alonso Fernández & Cristina Díaz Muñiz

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  1. Paulino José García-Nieto
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  3. José Ramón Alonso Fernández
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García-Nieto, P.J., García-Gonzalo, E., Alonso Fernández, J.R. et al. Modeling algal atypical proliferation in La Barca reservoir using L-SHADE optimized gradient boosted regression trees: a case study. Neural Comput & Applic 33, 7821–7838 (2021). https://doi.org/10.1007/s00521-020-05523-0

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