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Neural Computing and Applications
Published in: Neural Computing and Applications 13/2021

02-01-2021 | Original Article

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

Authors: Paulino José García-Nieto, Esperanza García-Gonzalo, José Ramón Alonso Fernández, Cristina Díaz Muñiz

Published in: Neural Computing and Applications | Issue 13/2021

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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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Metadata
Title
Modeling algal atypical proliferation in La Barca reservoir using L-SHADE optimized gradient boosted regression trees: a case study
Authors
Paulino José García-Nieto
Esperanza García-Gonzalo
José Ramón Alonso Fernández
Cristina Díaz Muñiz
Publication date
02-01-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 13/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05523-0

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