偏最小二乘回归模型在非点源负荷预测中的应用  

Application of partial least square regressive model in estimating the load of non-point source pollution

作　　者：李家科[1] 李怀恩[1] 李亚娇[1] 

机构地区：[1]西安理工大学水利水电学院,陕西西安710048

出　　处：《西北农林科技大学学报（自然科学版）》2007年第4期218-222,228,共6页Journal of Northwest A&F University(Natural Science Edition)

基　　金：国家自然科学基金重大研究计划项目(90610030);陕西省教育厅省级重点实验室重点科研计划项目(05JS35)

摘　　要：将偏最小二乘回归模型应用于流域非点源污染年负荷量预测,并与基于最小二乘的多元线性回归模型预测结果进行了对比。实例计算分析结果表明,偏最小二乘回归分析实现了多元回归、主成分分析和典型相关分析的综合,能较好地处理变量之间的多重相关性问题,建模所需样本少,且计算结果合理,具有较好的推广应用价值。The partial least square regressive method （PLS） is applied to build the model estimating the annual load of NSP,whose results are compared with the ones of least square method. It is showed that PLS integrating the multi-regression, the principal component analysis and typical correlation analysis,can easily solve the multiple correlation problems in the multiple linear regressive analysis,and only need a few of data to build the model. In addition,the calculation results of PLS is credible,and the estimated regressive parameters with PLS are robust. To sum up,the method is feasible and practical.

关 键 词：多元线性回归 偏最小二乘网归模型 非点源污染 污染负荷预测 

分 类 号：X143[环境科学与工程—环境科学]