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Environmental Science and Pollution Research

, Volume 25, Issue 23, pp 22658–22671 | Cite as

Cyanotoxin level prediction in a reservoir using gradient boosted regression trees: a case study

  • Paulino José García Nieto
  • Esperanza García-Gonzalo
  • Fernando Sánchez Lasheras
  • José Ramón Alonso Fernández
  • Cristina Díaz Muñiz
  • Francisco Javier de Cos Juez
Research Article

Abstract

Cyanotoxins are a type of cyanobacteria that is poisonous and poses a health threat in waters that could be used for drinking or recreational purposes. Thus, it is necessary to predict their presence to avoid risks. This paper presents a nonparametric machine learning approach using a gradient boosted regression tree model (GBRT) for prediction of cyanotoxin contents from cyanobacterial concentrations determined experimentally in a reservoir located in the north of Spain. GBRT models seek and obtain good predictions in highly nonlinear problems, like the one treated here, where the studied variable presents low concentrations of cyanotoxins mixed with high concentration peaks. Two types of results have been obtained: firstly, the model allows the ranking or the dependent variables according to its importance in the model. Finally, the high performance and the simplicity of the model make the gradient boosted tree method attractive compared to conventional forecasting techniques.

Keywords

Statistical machine learning techniques Regression trees Gradient boosting Cyanotoxins Cyanobacteria Harmful algal blooms (HABs) 

Notes

Acknowledgments

Authors wish to acknowledge Cantabrian Basin Authority (Ministry of Environment, Rural and Marine Affairs of Spain) for the dataset used in this research.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Paulino José García Nieto
    • 1
  • Esperanza García-Gonzalo
    • 1
  • Fernando Sánchez Lasheras
    • 2
  • José Ramón Alonso Fernández
    • 3
  • Cristina Díaz Muñiz
    • 3
  • Francisco Javier de Cos Juez
    • 4
  1. 1.Department of Mathematics, Faculty of SciencesUniversity of OviedoOviedoSpain
  2. 2.Department of Construction and Manufacturing EngineeringUniversity of OviedoGijónSpain
  3. 3.Cantabrian Basin AuthoritySpanish Ministry of Agriculture, Food and EnvironmentOviedoSpain
  4. 4.Exploitation and Prospecting DepartmentUniversity of OviedoOviedoSpain

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