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

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.