干旱区研究

干旱区研究 ›› 2013, Vol. 30 ›› Issue (6): 1144-1149.

• 生态农业 • 上一篇    

西安市耕地面积变化驱动力分析及动态预测

易浪，任志远，刘焱序   

  1. （陕西师范大学旅游与环境学院，陕西 西安 710062）
  • 收稿日期:2012-09-20 修回日期:2012-11-21 出版日期:2013-11-15 发布日期:2013-12-12
  • 通讯作者: 任志远．E-mail： renzhy@snnu.edu.cn
  • 作者简介:易浪（1989－），男，硕士研究生，主要从事资源环境遥感与GIS等方面的研究．E-mail： yilang5758@126.com
  • 基金资助:

    国家自然科学基金项目（41071057）；陕西师范大学中央高校基本科研专项基金（GK201101002）

Driving Forces and Dynamic Prediction of Cultivated Land Area Change in Xi’an

YI Lang ，REN Zhi-yuan ，LIU Yan-xu   

  1. (College of Tourism and Environmental Sciences, Shaanxi Normal University, Xi’an 710062,Shaanxi,China)
  • Received:2012-09-20 Revised:2012-11-21 Online:2013-11-15 Published:2013-12-12

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被引次数

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摘要： 利用主成分分析对指标因子进行特征提取，采用灰色预测方法来构建预测指标，运用MATLAB软件建立的BP神经网络进行耕地面积预测。结果表明:经主成分分析的BP神经网络模型具有结构简单、收敛速度快、精度高的特点，对西安市耕地资源的预测精度较高，可靠性较好，具有一定的可行性。预测结果显示，西安市2013年的年末耕地面积为248 826.67 hm2，而经济的高速发展和城市化水平是西安市耕地面积减少的主要原因，在西安市城市发展过程中耕地资源的规划和保护应该得到充分的重视。

关键词: 主成分分析, BP神经网络, 耕地预测, 西安

Abstract: The principal component analysis was used to extract the features of indicator factors, the grey prediction method was applied to construct the prediction indexes, and the MATLAB software and BP neural network were used to predict the change of area of cultivated land in Xi[JP8]’a[JP]n. The results showed that the BP neural network was characterized by the simple structure, fast convergence and high precision after analyzed with the principal component analysis. The accuracy of predicted annual reduction of area of cultivated land with the BP neural network was high, and the BP neural network was reliable and feasible. The predicted results showed that the area of cultivated land in Xi[JP8]’a[JP]n in 2013 was 248 826.67 hm2.The reduction of area of cultivated land in Xi[JP8]’a[JP]n was mainly caused by the rapid economic development and the increase of urbanization level. It was suggested to pay great attention to the planning and protection of cultivated land resources in Xi[’an in the urban development.

Key words: principal component analysis (PCA), BP neural network, prediction of cultivated land, Xi’an