摘要
支持向量机以统计学习理论为基础,采用结构风险最小化准则,将学习问题转化为一个凸二次规划问题,能够得到全局最优解,适合解决小样本、非线性分类及回归问题。根据水资源安全的内涵,筛选出具有代表性的指标,组成水资源安全评价指标体系。建立了基于支持向量机的水资源安全评价模型,将安全标准划分为良好、安全、临界、不安全、危险5个等级。根据水资源安全评价标准及所属评价等级值,随机生成样本集,180个样本作为训练样本,构造了5个两类支持向量分类器,20个样本作为检验样本,检验样本分类全部正确。将模型应用于山西省11个城市的水资源安全评价,结果表明,该方法是有效、可行的。
Based on statistical learning theory,support vector machine（SVM） can transform the learning process into a convex quadratic planning problem to get a global optimization by using the rule of structure risk minimization,which is appropriate to solving small sample,nonlinear classification and regression. Based on the concept of water resources security,representative indicators were selected for the water resources security assessment indicator system.Water resources assessment model based on support vector machine was established.Water resources security standards were divided into five grades,named good,safe,critical,not safe and dangerous.Sample sets were formed by stochastic method according to water resources security standards and their grade values.180 samples were used for training to construct 5 two-classification support vector classifiers.Twenty samples were used for testing and all of which can be classified correctly.Applying the model to 11 cities in Shanxi Province,the results show that the algorithm is reasonable and feasible.
出处
《自然灾害学报》
CSCD
北大核心
2011年第6期167-171,共5页
Journal of Natural Disasters
基金
教育部国家外国专家局111创新引智计划(B08039)
全球环境基金(GEF)(MWR-9-2-1)
关键词
统计学习理论
支持向量机
模式分类
水资源安全
statistical learning theory
support vector machine
pattern classification
water resources security
