关键词: 支持向量机, 遗传算法, 参数优选, 水质评价

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