基于PCA-MCAFA-LSSVM的养殖水质pH值预测模型

Forecasting Model for pH Value of Aquaculture Water Quality Based on PCA-MCAFA-LSSVM

为解决水质预测传统方法精度低、鲁棒性差等问题，提出了基于主成分分析（PCA）、改进文化鱼群算法（MCAFA）和最小二乘支持向量机（PCA-MCAFA-LSSVM）的养殖水质pH值预测模型。该模型通过主成分分析提取养殖生态环境指标的主成分，降低模型输入向量维数，利用改进文化鱼群算法对最小二乘支持向量机超参数进行组合优化，以自动获取最优超参数建立非线性养殖水质pH值预测模型。应用该模型对宜兴市河蟹养殖某池塘2011年9月1日～9月4日在线监测的水质数据进行了预测分析，试验结果表明：该模型取得较好的预测效果，与分别用蚁群算法或遗传算法优化LSSVM的方法相比，PCA-MCAFA-LSSVM模型有93.05%的测试样本绝对误差小于8%，最大绝对误差仅为11.61%，均方根误差、平均相对误差绝对值和运行时间分别为0.0474、0.0041和4.367s，且均优于其他预测方法。PCA-MCAFA-LSSVM算法不仅计算速度快、测精度高，还能够为河蟹养殖水质调控管理提供决策依据。

In order to solve the problem of low prediction accuracy and bad robustness of the traditional forecasting methods in water quality, this paper put forward the prediction model for pH value of aquaculture water quality based on the principal component analysis （PCA） and least squares support vector machine （LSSVM）, which the hyper-parameters is optimized by modified cultural artificial fish-swarm algorithm（MCAFA）. The dimension of aquiculture ecologic environmental data was reduced by principal component analysis; double evolutionary mechanism of cultural algorithm for reference was applied and LSSVM was taken as an artificial fish; belief space was used to guide the shoal evolution step size, global search direction and Cauchy mutation to improve the diversity of the artificial fish swarm; so the optimal hyper-parameters nonlinear pH value prediction model was automatically obtained. Based on the prediction model, the water quality on-line monitoring was predicted for a high-density aquaculture pond from September 1, 2011 to September 4, 2011 in Yixing city, Jiangsu province. Experimental results show that the PCA-MCAFA-LSSVM prediction model has good prediction effect than the ant colony algorithm LSSVM and genetic algorithm LSSVM. The absolute error of the 93.05% test samples is less than 8%, and the max absolute error is only 1161%; the root mean square error, average absolute relative error and the running time are 0.0474, 0.0041 and 4.367s respectively, which are better than those from the other models. It is obvious that PCA-MCAFA-LSSVM prediction model has low computational complexity and high forecast accuracy. It can provide the decision basis for the water quality controlling in the high density eriocheir sinensis culture.