Abstract: The paper establishs an assessment model of comprehensive water quality based on Support Vector Machine（SVM），and proposes a self-adaptive optimization algorithm for the selection of SVM classifier model parameters using float genetic algorithm.The model is experimented with monitoring water quality data of Wei River，and is compared with water quality evaluation methods of single factor assessment，Principal Components Analysis（PCA） and BP neural network.The results demonstrate that the proposed method can give a better quality comprehensive evaluation，and can reflect the water quality of rivers accurately and objectively from the overall.

Key words: Support Vector Machine（SVM）, Genetic Algorithm（GA）, parameter optimized, water quality evaluation

摘要： 建立了基于支持向量机的综合水质评价模型，构建了基于浮点数编码的遗传算法来优选模型参数，运用该模型以渭河水质监测数据为例进行了水质评价，并与水质评价的单因子法、主成分分析法和神经网络方法进行了分析比较。实验结果表明，该方法可以较好地实现水质综合评价，能从整体上准确、客观地反映河流水质情况。

关键词: 支持向量机, 遗传算法, 参数优选, 水质评价