Application of GA-SVM Prediction in Region Coordinated Development of Economy-Resource-Environment System: County-Level Evidence from China Guanzhong Urban Agglomeration

County Economy-Resource-Environment coordinated development analysis and prediction play an important role in region economic development and improve the transformation of national economic growth pattern. According to the county Economy-Resource-Environment composite system data which is large scale and imbalance, this paper presented a support vector machine (SVM) model to predict county Economy-Resource-Environment coordinated development degree. In order to improve the discrimination precision of SVM in prediction, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The proposed GA-SVM method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding county Economy-Resource-Environment coordinated development degree prediction for China Guanzhong urban agglomeration. The result shows that the improved SVM has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county Economy-Resource-Environment coordinated development degree classification and prediction.