Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain)

作者：Garcia Nieto P J; Alonso Fernandez J R; de Cos Juez F J; Sanchez Lasheras F; Diaz Muniz C

来源：Environmental Research, 2013, 122: 1-10.

DOI：10.1016/j.envres.2013.01.001

• Statistical machine learning techniques
• Cyanobacteria
• Cyanotoxins
• Genetic algorithms (GAs)
• Support vector regression (SVR)

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained.

• 出版日期2013-4