摘要
运用数据挖掘方法,对文献中收集的铜绿微囊藻试验模拟数据进行深入分析。结果表明,数据挖掘方法中的主成分分析作为非参数的分类方法可以应用于识别变量的重要性。同时得到影响室内铜绿微囊藻生长的主要因素是初始pH(pH0)、接种藻密度(N0)和初始总磷浓度(TP0);适当减小藻类的N0、水体的pH0或TP0都可以抑制铜绿微囊藻的生长。说明数据挖掘方法能够对铜绿微囊藻试验模拟数据进行定性分析。
The data mining method was used to deeply analyze Microcystis aeruginosa experimental simulation data collected from literatures. The results demonstrated that principal component analysis, served as a non-parametric method of classification, could be used to identify the important variables. In addition, the primary factors which influenced the growth of Microcystis aeruginosa were initial pH （ pH0 ）, algal density （ N0 ） and total phosphorus （TP0）. The growth of Microcystis aeruginosa might be inhibited by reducing the value of N0, pH0 or TPo. This indicated that the data mining method could make qualitative analysis for experimental simulation data of Microcystis aeruginosa.
出处
《环境工程技术学报》
CAS
2012年第4期309-312,共4页
Journal of Environmental Engineering Technology
基金
国家水体污染控制与治理科技重大专项(2009ZX07106-001)
关键词
数据挖掘
试验模拟数据
富营养化
铜绿微囊藻
藻密度
data mining
experimental simulation data
eutrophication
Microcystis aeruginosa
algal density
