关键词: 水质预测, 偏最小二乘回归, 支持向量机, 预测模型, 粒子群优化算法

Abstract: Concerning the problem of low prediction accuracy because of multiple correlation factor in the traditional water quality prediction method, this paper introduces a partial least squares and support vector machine coupled method—the water quality prediction method（PLS-SVM）. Using partial least squares method extracts the variable component with strong influence, overcoming the information redundancy and reducing the dimension of support vectors. And using support vector machine modeling can be a better solution to the problem of high-dimensional nonlinear small samples. And using improved PSO algorithm to optimize SVM parameters reduces the parametric searching blindness. The results show that the coupled model fitting and forecasting accuracy is significantly better than the commonly used BP artificial neural networks and traditional SVM, can be better used in water quality prediction.

Key words: prediction of water quality, partial least squares regression, support vector machine, prediction model, Particle Swarm Optimization（PSO）