CAJ | 학술논문

嘉陵江草街水库自建成后20112013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。
가릉강초가수고자건성후20112013년련속3년발생갑조수화현상，급당지경제발전화생태안전대래영향。근거2011년5월지2013년7월초가수고대패상、하유8개단면적축월조사수거，이용지지향량궤재처리소양본문제、비선성분류문제화범화추엄방면적우세，구건료기우지지향량궤분류적초가수고갑조수화예경모형。결과표명，이용본월이화수거화본월예씨의다갑조（ Peridiniopsis niei）밀도수거건립적모형，대측시양본취득료80％이상적판별정학솔，차대갑조수화양본적판별정학솔위100％。인차，지지향량궤작위신흥적궤기학습방법，가이위배경관리부문발포수화예경신식제공과학의거，병재배경보호영역구유엄활적응용전경。
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 abun-dance of current month, and accuracy arrived at 100% for dinoflagellate bloom samples.The results showed that the SVM classifi-cation is an effective new way that can be used in monitoring dinoflagellate bloom in Caojie Reservoir and have a promising applica-tion prospect for environmental protection .