基于粗糙集融合支持向量机的水质预警模型

为解决因水质预警耦合因素多,预警模式复杂以及信息不完整所引起的水质预警精度低问题,提出了粗糙集融合支持向量机(RS-SVM)的水质预警模型.首先采用粗糙集对14个初始预警指标进行属性约简,去除冗余或干扰特征,得到基于5个核心预警指标的数据集,以此数据集对支持向量机进行训练优化,构建RS-SVM水质预警模型.运用该模型对江苏宜兴市集约化河蟹养殖池塘水质进行预警,实证对比分析,对于不同的警度级别,预警精度都在91%以上,与标准支持向量机和BP神经网络模型相比,该模型不仅具有计算效率高、预警性能好,且预警结果与实际情况比较吻合,为集约化水产养殖水质预警提供了一种新思路.

Water quality early-warning model based on support vector machine optimized by rough set algorithm

A new early warning model of water quality, combining rough set (RS) and support vector machine (SVM), is presented to improve the prediction precision affected by mass coupling factors, complex mode and information loss. Firstly, a core warning set based on 5 factors is obtained by using RS to deduct the redundant and disturbed properties from the initial set based on 14 factors. Consequently, the early warning model of water quality based on RS-SVM is built up by the core warning set. The experimental results show that our method improves the precision to more than 91% in any warning level by using the water quality data obtained from Yixing, Jiangsu province. Compared with the standard SVM and BP neural networks, the new model not only has effectiveness of calculation and prediction, but also provides warning results with practicality. This model demonstrates a new thought of early warning on intensive aquaculture water quality.