基于主成分分析与支持向量回归的精明增长建模与预测

doi:10.12052/gdutxb.170073

Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (05): 29-33.doi: 10.12052/gdutxb.170073

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Smart Growth Modeling and Prediction Based on Principle Component Analysis and Support Vector Regression

Cai Nian, Li Fei-yang, Chen Wen-jie, Chen Wei-jian

School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China

Received:2017-03-22 Online:2017-09-09 Published:2017-07-10

Abstract

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.

Key words: smart growth, principle component analysis, support vector regression

CLC Number:

TP391.9

Cai Nian, Li Fei-yang, Chen Wen-jie, Chen Wei-jian. Smart Growth Modeling and Prediction Based on Principle Component Analysis and Support Vector Regression[J].Journal of Guangdong University of Technology, 2017, 34(05): 29-33.

References 14

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