Prediction of Arable Land Change with Artificial Neural Network Model for Chongqing Municipality in China

摘要：

Arable land has been decreasing due to rapid population growth and economic development as well as urban expansion. To obtain a better understanding of controlling land use and to design mechanisms to ensure sustainable land management, an accurate prediction of arable land is a key issue fundamentally. In this study, artificial neural network (ANN) model is applied to estimate the arable land change for Chongqing Municipality in China. The prediction is implemented using a feed-forward neural network, trained by back-propagation algorithm. In order to investigate the socioeconomic influences on arable land reduction, the ANN model is trained based on population, gross domestic production and fixed assets investment. Further, the prediction results from ANN model and linear regression model are compared to test the performance of model validation. Consequently, artificial neural network shows the ability to catch non-linear relationships between arable land change and socioeconomic factors and to predict arable land change with a high degree of accuracy. In conclusion, the ANN model is applicable of predicting arable land change and some suggestions would be provide for researchers and decision makers.

关键词： arable land driving force prediction artificial neural network Chongqing

作者: Dai Fuqiang Chen Ke Zhou Xu Jiang Liangqun

作者单位: Land and Resources College,China West Normal University CWNU Nanchong,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 11-15

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）