基于粒子群算法优化支持向量回归的水质预测模型

Water Quality Prediction Model Based on Particle Swarm Optimization Support Vector Regression

溶解氧是反映水污染程度的一个重要指标,准确的预测可以高效合理地判断水质环境的状况。由于水质环境的实时变化和复杂性以及收集数据的偏差,在水生系统中获得高效、精确的预测模型是困难的。因此,首先利用主成分分析(PCA)确定影响水质溶解氧的变量数目,降低数据维数,为解决变量间的非线性和非平稳性问题,提出用互信息(MI)选取影响强的因素作为预测模型的输入变量。然后利用一种基于高斯函数的非线性递减权重的粒子群算法优化支持向量回归(GNIPSO-SVR)模型中的参数选择过程,有效克服传统SVR预测模型的参数选择问题,并考虑空气中的污染物因素,构建污染物影响的GNIPSO-SVR模型。然后将该模型应用于上海的水质溶解氧的预测中,把GNIPSO-SVR模型与BP神经网络、支持向量回归机(SVR)、粒子群算法优化支持向量回归机(PSO-SVR)的模型对比分析,结果表明,提出的方法可以有效解决溶解氧变量间的冗余性与相关性问题,提高预测精度和运行速度。

Dissolved oxygen is one of the essential indexes reflecting the degree of water pollution,and thus,it can accurately,efficiently,and reliably predict the state of water quality.Obtaining efficient and accurate prediction models in the aquatic system is difficult because of the real-time variation and complexity of the water quality environment and the biases in data collection.Therefore,principal component analysis is first used to determine the number of variables that affect dissolved oxygen in water quality,where the data’s dimension is reduced.To solve the problem of nonlinearity and non-stationarity among the variables,mutual information is proposed to select the factors that strongly influence this relationship as the input variables of the prediction model.Next,a nonlinear decreasing weight particle swarm optimization algorithm with Gaussian function is used to optimize the parameter selection process of a support vector regression(GNIPSO-SVR)model,which effectively overcomes the parameter selection limitations of the traditional SVR prediction model.To consider the effect of air pollutants,a GNIPSO-SVR model is constructed.This model is then employed to predict dissolved oxygen in the water quality of Shanghai.Comparing and analyzing the GNIPSO-SVR model results with those obtained from the BP neural network,the SVR,and PSO-SVR models,the proposed model effectively eliminated the redundancy and correlation problems among dissolved oxygen variables and improved the prediction accuracy and operating speed.