Predict Coordinated Development Degree of County Eco-Environment System Using GA-SVM: A Case Study of Guanzhong Urban Agglomeration

Predict Coordinated Development Degree of County Eco-Environment System Using GA-SVM: A Case Study of Guanzhong Urban Agglomeration

Jing Zhao (School of Economics and Management, Xi'an University of Technology, Xi'an, China & Urban Economic and Management Research Center, Xi'an University of Technology, Xi'an, China) and Zhen Jin (School of Economics and Management, Xi'an University of Technology, Xi'an, China)
Copyright: © 2018 |Pages: 10
DOI: 10.4018/JGIM.2018070101

Abstract

This article describes how economic development has had a significant impact on the environment. County eco-environment coordinated development has contributed to regional coordinated development in China. A support vector machine (SVM) model was constructed to classify and predict coordinated development degrees of the county eco-environment system. In order to improve the discrimination precision of SVM in classification, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding coordinated development degree of county eco-environment system prediction for Guanzhong urban agglomeration. It found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient. The simulation indicates that the county slowing-down of economic development would not have positive effect on the environment sustainability. GA-SVM provides an effective measurement for region eco-environment system classification and prediction.
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2. Methodology

The support vector machine (SVM) first proposed by Cortes & Vapnik is gaining popularity because of its excellent properties of high generalization performance and global optimal solution (Cortes & Vapnik, 1995). Not only its structure is simple, but also its various technical capabilities is obviously boosted, especially the generalization ability. The detailed explanation and proof of SVM may be contained in the book (Ukil, 2007).

For given training set:

,
,

First, through nonlinear transform of ,the paper mapped the input space into Hilbert space (Qin, S. J., & Badgwell, T. A., 2003), to construct the optimal linear function:

(1)

Thus, it could get the linear approximation in feature space, Vapnik advanced that could take -insensitive loss function as measurement of approximation:

(2)

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