基于APSO-WLSSVR的水质预测模型

为解决传统预测方法和标准最小二乘支持向量回归机(least squares support vector regression, LSSVR)在水质预测中存在预测精度低、鲁棒性差等问题,提出了自适应粒子群优化加权最小二乘支持向量回归机(adaptive particle swarm optimization weighted least squares support vector regression, APSO-WLSSVR)的水质预测模型。根据样本对模型重要性不同为各样本赋予不同权重,建立了加权最小二乘支持向量回归机(weighted least squares support vector regression, WLSSVR),实现对样本数据“重近轻远” 的优化选择,避免标准LSSVR算法因没有考虑样本重要性差异致使预测精度低的问题;采用自适应粒子群优化算法对模型参数组合进行优化选择,克服了标准LSSVR算法因试凑法获取参数的盲目性和人为因素的影响。为验证该模型的性能,对江苏省宜兴市集约化河蟹养殖水质进行预测,并与其他预测方法对比分析,结果表明该模型预测精度明显提高,还具有较好的鲁棒性和泛化能力,能够满足集约化水产养殖水质管理的实际需要。

Water quality prediction model based on APSO-WLSSVR

In order to solve the problem of low prediction accuracy, the bad robustness of the traditional forecasting methods and the standard least squares support vector regression (LSSVR) in water quality prediction, the adaptive particle swarm optimization weighted least squares support vector regression (APSO-WLSSVR) model for water quality prediction was proposed. Different weights were set for various samples according to its different importance for the model. A weighted least squares support vector regression model (WLSSVR) was established, which could avoid low prediction accuracy of the standard LSSVR, and ignore the importance of the samples differences. The particle swarm optimization algorithm was adopted to optimize and choose the model parameters to reduce the blindness and the impact of human factors of the standard LSSVR trial method when obtaining the parameters. In order to verify the performance of the model, the water quality of intensive farming river crab in Yixing, Jiangsu Province, was predicted, which was also compared with other forecast methods. The results showed that the model prediction accuracy was obviously improved, and also had good robustness and generalization ability, which could met the practical needs of the intensive aquaculture water quality management.