基于主成分和粒子群优化支持向量机的水质评价模型

水质的评价是治理水污染必不可少的工作。为了准确、快速地对水质进行评价，利用主成分分析法从水质监测常见的多个物化指标提取出主成分，然后将主成分作为支持向量分类机的输入，利用历史数据进行水质评价训练并用粒子群算法优化参数，构造出水质评价模型，将从物化指标中得出的主成分代入此模型即可得到水质类别。最后，选取水质监测点实测数据进行试验，结果表明，模型的水质评价结果准确且稳定。

Water quality evaluation mode based on principal component analysis and support vector machine optimized by PSO

Water quality evaluation is essential to control the water pollution.We extract the main components from several physical and chemical indicators in water quality monitoring by the use of principal component analysis,and then put them into the support vector machine to evaluate the water quality with the help of historical statistics,and optimize the parameters by using the particle swarm algorithm,as a result of which the mode is built.By plugging the principal components from the indicators to the built mode,the categories of the water quality will be obtained.At last,it's proved that the results of the water quality are correct and stability by conducting the experiment on the real samples from the monitoring station.