@inproceedings{4a2b2c9bda0a43b8b23882c4781b0c95,
title = "Study on the driving forces and prediction of built-up area for urban expansion in Kunming",
abstract = "Based on the analysis of driving forces of urban land expansion by Principal component analysis (PCA), this paper established a predicting model of urban built-up area for future by using socio-economical data. Being good at the performance of nonlinear approximation, artificial neural network (ANN), especially the back propagation algorithm (BP), is applied in the prediction of bulit-up land and had attained satisfactory results. Taking Kunming for example, the results showed that the urbanization is the decisive factor influencing urban land expansion, and a predicting model combined PCA and BP-ANN used to predict urban built-up area in the year of 2009-2015. The method employed in this paper can provide a reference to study on urban land expansion for urban development and planning in the inland cities lacking of multi-sources data.",
keywords = "BP neural network, Built-up area, Driving forces, Kunming, PCA, Urban land expansion",
author = "Yulian Hong and Jianhua Xu and Zhanyong Wang",
year = "2011",
doi = "10.1109/RSETE.2011.5965098",
language = "英语",
isbn = "9781424491711",
series = "2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings",
pages = "3569--3572",
booktitle = "2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings",
note = "2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 ; Conference date: 24-06-2011 Through 26-06-2011",
}
