广东工业大学学报2017，Vol.34Issue(5)：29-33,5.DOI:10.12052/gdutxb.170073
基于主成分分析与支持向量回归的精明增长建模与预测
Smart Growth Modeling and Prediction Based on Principle Component Analysis and Support Vector Regression
摘要
Abstract
With the urbanization extending at a high speed, the sustainable development of cities becomes a significant agenda for government policy makers. In order to effectively develop the strategy of smart growth, an evaluation model is proposed. First, principle component analysis (PCA) is applied to quantify the level of smart growth. Then, support vector regression (SVR) is employed to predict annual variation tendency of each indicator of smart growth. Finally, the total scores of smart growth are calculated for selecting an optimal solution to smart growth. The experiment results show that the proposed evaluation model can accurately measure the level of smart growth and predict the situation of smart growth in the future, which provides a comprehensive decision guidance for rational and healthy development of cities.关键词
精明增长/主成分分析/支持向量回归Key words
smart growth/principle component analysis/support vector regression分类
信息技术与安全科学引用本文复制引用
蔡念,李飞洋,陈文杰,陈伟建..基于主成分分析与支持向量回归的精明增长建模与预测[J].广东工业大学学报,2017,34(5):29-33,5.基金项目
广州市产学研协同创新重大专项项目(201508010001,201604016022,201604016064) （201508010001,201604016022,201604016064）
