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基于RBF神经网络的成都市城镇建设用地需求预测
  • ISSN号:1007-7588
  • 期刊名称:《资源科学》
  • 时间:0
  • 分类:TU984.113[建筑科学—城市规划与设计]
  • 作者机构:[1]四川农业大学资源环境学院,成都611130, [2]成都市国土规划地籍事务中心,成都610072
  • 相关基金:国土资源部公益性行业科研专项项目(编号:201211050)
中文摘要:

以成都市为研究区域,利用城镇建设用地和社会经济统计数据,从因素和时间二维角度出发,分别选用RBF神经网络模型和GM(1,1)模型,在模型比较优选的基础上对成都市城镇建设用地需求进行了预测。结果表明:RBF神经网络模型与GM(1,1)模型相比,前者的平均绝对误差和误差均方根较低,且线性拟合效果更佳,是一种精度较高的预测方法;据预测,近期2015年和远期2020年研究区城镇建设用地需求量将分别达到145 986.10hm2和182 321.26hm2,研究结果既能为具有类似数据"突变"特征的城市开展城镇建设用地需求预测提供方法借鉴,又能为研究区土地资源的可持续利用和土地利用总体规划的定期修编提供决策依据。

英文摘要:

With rapid urbanization and industrialization China is facing great urban construction land pressures. The expansion of construction land has also become one of the greatest features of Chinese land-use change. Chengdu is a city under rapid development and transformation in southwest China, however, the area of construction land shows a particular mutation feature. Here we focus on the prediction of urban construction land demand based on comparison and selection between a RBF neural network model and a GM (1, 1) model from two dimensions (factor and time). In order to reach a precise prediction, data about constriction land and social economy is applied. We found that the RBF neural network model has a better performance than the GM (1,1) model: the former has a lower absolute error and root mean square error, and performs better in linear fitting than the latter. By 2015, Chengdu' s construction land demand will reach 145 986.10hm^2 and by 2020 will reach 182 321.26hm^2. With the construction of an urban-rural infrastructure system and Tianfu district, it is estimated that construction land demand for both urban and rural areas in Chengdu will increase rapidly. The findings will provide a reference for urban construction land demand forecasting for city planners faced with similar 'mutation-featured' data. These findings will also be vital to the periodic revision of land-use planning in Chengdu.

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期刊信息
  • 《资源科学》
  • 中国科技核心期刊
  • 主管单位:中国科学院
  • 主办单位:中国科学院地理科学与资源研究所
  • 主编:成升魁
  • 地址:北京安定门外大屯路甲11号
  • 邮编:100101
  • 邮箱:zykx@igsnrr.ac.cn
  • 电话:010-64889446
  • 国际标准刊号:ISSN:1007-7588
  • 国内统一刊号:ISSN:11-3868/N
  • 邮发代号:82-4
  • 获奖情况:
  • 国内外数据库收录:
  • 日本日本科学技术振兴机构数据库,中国中国人文社科核心期刊,中国中国科技核心期刊,中国北大核心期刊(2004版),中国北大核心期刊(2008版),中国北大核心期刊(2011版),中国北大核心期刊(2014版),中国北大核心期刊(2000版)
  • 被引量:42316