基于支持向量机分类的嘉陵江草街水库甲藻水华预警  

Research on early warning of dinoflagellate bloom in Caojie Reservoir base on support vector machine classification

作　　者：刘朔孺[1,2] 杨敏[1] 张方辉[1] 张晟[1] 

机构地区：[1]重庆市环境科学研究院,重庆401147 [2]重庆大学城市建设与环境工程学院,重庆400045

出　　处：《湖泊科学》2015年第1期38-43,共6页Journal of Lake Sciences

基　　金：重庆市环境保护局环保科技项目(环科字2012第02号);重庆市基本科研业务费计划项目(2013cstc-jbky-01604)联合资助

摘　　要：嘉陵江草街水库自建成后2011-2013年连续3年发生甲藻水华现象,给当地经济发展和生态安全带来影响.根据2011年5月至2013年7月草街水库大坝上、下游8个断面的逐月调查数据,利用支持向量机在处理小样本问题、非线性分类问题和泛化推广方面的优势,构建了基于支持向量机分类的草街水库甲藻水华预警模型.结果表明,利用本月理化数据和本月倪氏拟多甲藻(Peridiniopsis niei)密度数据建立的模型,对测试样本取得了80%以上的判别正确率,且对甲藻水华样本的判别正确率为100%.因此,支持向量机作为新兴的机器学习方法,可以为环境管理部门发布水华预警信息提供科学依据,并在环境保护领域具有广阔的应用前景.Dinoflagellate bloom consecutively occurred in Caojie Reservoir from 2011 to 2013 and threatened the local economy and ecology. Recently,support vector machine（ SVM） was reported to have advantages of only requiring a small amount of samples,high degree of prediction accuracy,and generalization to solve the nonlinear classification problems. In this study,the SVM-based prediction model for dinoflagellate bloom was established by monthly field date collected from May 2011 to July 2013 at 8 transects in Caojie Reservoir. The maximum accuracy excessed 80% by choosing environmental variables data and Peridiniopsis niei abundance of current month,and accuracy arrived at 100% for dinoflagellate bloom samples. The results showed that the SVM classification is an effective new way that can be used in monitoring dinoflagellate bloom in Caojie Reservoir and have a promising application prospect for environmental protection.

关 键 词：支持向量机 甲藻水华 草街水库 倪氏拟多甲藻 

分 类 号：X524[环境科学与工程—环境工程]