Comparison of GM(1,<em>n</em>) and BP Neural Network Model in Predicting Construction Lands in Siping City, Jilin Province

MENG Xiangjian; LI Xiuxia

doi:10.13961/j.cnki.stbctb.2017.01.031

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Comparison of GM(1,n) and BP Neural Network Model in Predicting Construction Lands in Siping City, Jilin Province

- Comparison of GM(1,n) and BP Neural Network Model in Predicting Construction Lands in Siping City, Jilin Province
- Bulletin of Soiland Water Conservation Vol. 37, Issue 1, Pages: 173-176(2017)
- 作者机构：
  
  吉林师范大学旅游与地理科学学院,吉林,四平,136000
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2017.01.031
  CLC：
- Published：2017
- 稿件说明：
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MENG Xiangjian, LI Xiuxia. Comparison of GM(1,n) and BP Neural Network Model in Predicting Construction Lands in Siping City, Jilin Province[J]. Bulletin of Soiland Water Conservation, 2017, 37(1): 173-176.
DOI：

MENG Xiangjian, LI Xiuxia. Comparison of GM(1,n) and BP Neural Network Model in Predicting Construction Lands in Siping City, Jilin Province[J]. Bulletin of Soiland Water Conservation, 2017, 37(1): 173-176. DOI： 10.13961/j.cnki.stbctb.2017.01.031.

摘要

[目的] 科学准确地预测城市建设用地，有利于把握城市发展的速度，了解城市化发展进程，为相关政府部门掌握土地利用情况，制定土地总体规划提供科学依据。[方法] 把四平市作为研究对象，从“城市—农村”合力视角构建影响因子，利用因子分析探讨影响建设用地扩张的相关性，对指标进行筛选，在此基础上利用BP神经网络和灰色模型对四平市2012，2013和2014年建成区面积进行预测，最后对预测结果进行比较分析。[结果] 通过预测与比较分析可知，BP神经网络结果的相对误差分别为0.8%，1.1%和2%，而灰色GM（1.1）模型预测结果相对误差分别为0.04%，0%和3.2%。可以看出，BP神经网络预测出的结果与实际相比较误差均在2%以内。[结论] BP神经网络预测的结果较精确，运用该方法可以有效提高预测的精度。

Abstract

[Objective] The paper aims to compare the accurracy of BP neutral network and GM(1

n) in predicting construction land changes

which is beneficial to understand the urban development and provide refernces for general land planning. [Methods] With Siping City as the research object

we selected impact factors with the perspective of “city-rural integration” and used factor analysis to estimate the influence of construction land expansion and choose indicators. We then simulated and compared the predictions of construction land in 2012

2013 and 2014 in Siping City using the BP neural network and grey model. [Results] The relative error with BP neural network was 0.8%

1.1% and 2%

and the gray GM(1.1) model was 0.04%

0% and 3.2% respectively. The BP neural network are better than GM(1.1) model. [Conclusion] BP neural network can provide a higher accuracy.

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references

郑峰.海南省年土地2000需求结构预测及土地宏观开发战略研究[J].资源科学,1994,16(1):20-28.

陈国建,刁承泰,黄明星,等.重庆市区城市建设用地预测研究[J].长江流域资源与环境,2002,11(5):404-408.

孙秀锋,刁承泰,何丹.我国城市人口、建设用地规模预测[J].现人文城市研究,2005,12(10):18-51.

朱明仓,杨韬.城镇建设用地需求预测与配置研究[J].资源开发与市场,2007,23(1):10-12.

高凯,雷磊.土地利用规划中建设用地需求量预测模型研究:以葫芦岛市为例[J].社会发展,2010,12(6):64-65.

唐晶,周勇.基于灰色系统理论的城镇建设用地预测模型的研究:以湖北潜江市为例[J].安徽农业科学,2008,36(24):10607-10609.

王希营,付梅臣,刘宗强.土地利用规划中建设用地需求量预测的两种模型比较:以东方市为例[J].资源与产业,2009,11(5):87-91.

朱希刚.土地利用总体规划中建设用地需求量预测模型研究:以克拉玛依市为例[D].乌鲁木齐:新疆大学,2010.

闫闪闪,王卫伟,余迪毅.基于灰色系统的湖北黄石市建设用地预测[J].农业与技术,2012,32(5):166-168.

张晓瑞,方创琳,王振波,等.基于RBF神经网络的城市建成区面积预测研究:兼于BP神经网络和线性回归对比分析[J].长江流域资源与环境,2013,22(6):691-697.

钟珞,饶文碧,邹承明.人工神经网络及其融合应用技术[M].北京:科学出版社,2007.

郭杰,欧名豪,刘琼,等.基于BP神经网络的南通市建设用地需求预测[J].资源科学,2009,31(8):1355-1361.

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Comparison of GM(1,n) and BP Neural Network Model in Predicting Construction Lands in Siping City, Jilin Province

DOI：10.13961/j.cnki.stbctb.2017.01.031

摘要

Abstract

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