Forecasting and evaluating water quality of Changjiang River based on composite least square SVM with intelligent genetic algorithms
基于智能遗传算法与复合最小二乘支持向量机的长江水质预测与评价*

Forecasting and evaluation water quality is a complicated problem due to its nonlinearity and uncertainty. Least square support vector machine(LSSVM) has been successfully employed to solve regression and time series problem. This paper proposed a novel IGALSSVM model.The model based on a new genetic algorithm, intelligent genetic algorithm to optimize the parameters of LSSVM. In addition, applied the model to classify and forecast water quality of Changjiang River. Experimental results show that IGALSSVM model performs better than neural networks ,implying that IGALSSVM is very practical.