| 基于支持向量机分类的嘉陵江草街水库甲藻水华预警 |
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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[J].湖泊科学,2015,27(1):38-43. |
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| 作者姓名： | 刘朔孺 杨敏 张方辉 张晟 |
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| 作者单位： | 1. 重庆市环境科学研究院，重庆401147; 重庆大学城市建设与环境工程学院，重庆400045
2. 重庆市环境科学研究院,重庆,401147 |
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| 基金项目： | 重庆市环境保护局环保科技项目，重庆市基本科研业务费计划项目 |
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| 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.
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| 关 键 词： | 支持向量机 甲藻水华 草街水库 倪氏拟多甲藻 |
| 收稿时间： | 1/2/2014 12:00:00 AM |
| 修稿时间： | 2014/5/27 0:00:00 |
Research on early warning of dinoflagellate bloom in Caojie Reservoir base on support vector machine classification |
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| LIU Shuoru,YANG Min,ZHANG Fanghui and ZHANG Sheng.Research on early warning of dinoflagellate bloom in Caojie Reservoir base on support vector machine classification[J].Journal of Lake Science,2015,27(1):38-43. |
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| Authors: | LIU Shuoru YANG Min ZHANG Fanghui and ZHANG Sheng |
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| Institution: | Chongqing Academy of Environmental Science, Chongqing 401147, P. R. China;Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400045, P. R. China,Chongqing Academy of Environmental Science, Chongqing 401147, P. R. China,Chongqing Academy of Environmental Science, Chongqing 401147, P. R. China and Chongqing Academy of Environmental Science, Chongqing 401147, P. R. China |
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| 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. |
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| Keywords: | Support vector machine dinoflagellate bloom Caojie Reservoir Peridiniopsis niei |
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