%0 Journal Article %T 基于支持向量机分类的嘉陵江草街水库甲藻水华预警 %T Research on early warning of dinoflagellate bloom in Caojie Reservoir base on support vector machine classification %A 刘朔孺 %A 杨敏 %A 张方辉 %A 张晟 %A LIU,Shuoru %A YANG,Min %A ZHANG,Fanghui %A ZHANG,Sheng %J 湖泊科学 %J Journal of Lake Sciences %@ 1003-5427 %V 27 %N 1 %D 2015 %P 38-43 %K 支持向量机;甲藻水华;草街水库;倪氏拟多甲藻 %K Support vector machine;dinoflagellate bloom;Caojie Reservoir;Peridiniopsis niei %X 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. %X 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. %R 10.18307/2015.0105 %U http://www.jlakes.org/ch/reader/view_abstract.aspx %1 JIS Version 3.0.0