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引用本文:刘朔孺,杨敏,张方辉,张晟.基于支持向量机分类的嘉陵江草街水库甲藻水华预警.湖泊科学,2015,27(1):38-43. DOI:10.18307/2015.0105
LIU Shuoru,YANG Min,ZHANG Fanghui,ZHANG Sheng.Research on early warning of dinoflagellate bloom in Caojie Reservoir base on support vector machine classification. J. Lake Sci.2015,27(1):38-43. DOI:10.18307/2015.0105
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基于支持向量机分类的嘉陵江草街水库甲藻水华预警
刘朔孺1,2,杨敏1,张方辉1,张晟1
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
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.
关键词:  支持向量机  甲藻水华  草街水库  倪氏拟多甲藻
DOI:10.18307/2015.0105
分类号:
基金项目:重庆市环境保护局环保科技项目(环科字2012第02号)和重庆市基本科研业务费计划项目(2013cstc-jbky-01604)联合资助
Research on early warning of dinoflagellate bloom in Caojie Reservoir base on support vector machine classification
LIU Shuoru,YANG Min,ZHANG Fanghui,ZHANG Sheng
Abstract:
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.
Key words:  Support vector machine  dinoflagellate bloom  Caojie Reservoir  Peridiniopsis niei
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