Research on the forecast of cultivated land variation trend in rapidly urbanization area-A case study of Wujiang city
The city of Wujiang, which locates at the center of the Yangtze River delta, is a relatively ideal case for study because it is a typical area that experiences the rapid urbanization. According to the statistical and survey data at county level during the past 20 years, this article establishes a predicting model by comprehensively using both PCA and BP neural networks. Principal component analysis is firstly used to preprocess input variables in order to raise the networks operational efficiency. While establishing the model of BP neural networks, it uses the data from 1990-2004 as the learning samples and that from 2005-2007 as the testing samples. The results show that the relative errors between the predicted value and the actual value are all less than 1.15％, which indicates that the neural network technique has a big power in the study of forecasting of cultivated area and the train of thought is reasonable. Finally the model established is used to do the simulated predication of the cultivated area for the year of 2010 in Wujiang, and the result shows that under the guidance of current policy, the decreasing rate of the cultivated area in the city of Wujiang dramatically drops.
cultivated land principal component analysis (PCA) BP neural network variation trend
Qian Li Xu Yannan Wang Xi
College of Forest Resources and Environment Nanjing Forestry University Nanjing, 210037, China Goizueta Business School Emory University Atlanta, Georgia,30322,USA
国际会议
Third International Conference on Information and Computing(第三届信息与计算科学国际会议 ICIC 2010)
无锡
英文
188-191
2010-06-04（万方平台首次上网日期，不代表论文的发表时间）