Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain.
Sci Total Environ. 2012 Jul 15;430:88-92. doi: 10.1016/j.scitotenv.2012.04.068. Epub 2012 May 24.
Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational water uses. The aim of this study is to improve our previous and successful work about cyanotoxins prediction from some experimental cyanobacteria concentrations in the Trasona reservoir (Asturias, Northern Spain) using the multivariate adaptive regression splines (MARS) technique at a local scale. In fact, this new improvement consists of using not only biological variables, but also the physical-chemical ones. As a result, the coefficient of determination has improved from 0.84 to 0.94, that is to say, more accurate predictive calculations and a better approximation to the real problem were obtained. Finally the agreement of the MARS model with experimental data confirmed the good performance.
蓝藻毒素是由蓝藻产生的一种有毒物质,会对饮用水和娱乐用水的健康造成危害。本研究旨在改进我们之前在西班牙北部阿斯图里亚斯的特罗纳水库中利用多元自适应回归样条(MARS)技术从一些实验性蓝藻浓度预测蓝藻毒素的成功工作,在局部尺度上利用 MARS 技术。实际上,这种新的改进不仅使用了生物变量,还使用了物理化学变量。结果表明,决定系数从 0.84 提高到 0.94,也就是说,预测计算更加准确,对实际问题的近似程度更好。最后,MARS 模型与实验数据的一致性证实了其良好的性能。