The increase of urban built-up area is propelled by many factors of society, economy and urban environment. So it is difficult to predict the urban built-up area by traditional methods. Having good performance of nonlinear approximation, artificial neural network (ANN), especially the back propagation algorithm (BP), is applied widely in many predictions and has very satisfactory effects. Principal component analysis(PCA) can reduce the dimensions of data, while maintaining the data characteristic effectively. It is integrated with BP neural network at data input port. By decreasing the number of input neuron, it can enhance the network performance and improve the prediction. Taking Beijing for example, this article establishes a predicting model by using both PCA and BP neural networks, and makes the prediction of urban built-up area for 2005. The model's learning samples are social, economic and environmental statistics in 1986~2003, and the testing sample is statistics in 2004. The results show that the relative error between the value predicted by the BP neural network based on PCA and the actual value is only 2.8%, and the BP neural network based on PCA has higher precision and better effectiveness than traditional BP neural network.
principal component analysis (PCA)BP neural networkurban built-up areapredictionBeijing
NETL
NSTL
维普
万方数据
