东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

Transforming the Loess Plateau of China

ISSN号：1000-0585
期刊名称：《地理研究》
时间：0
分类：TU984.113[建筑科学—城市规划与设计] TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
作者机构：[1]Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China, [2]School of Geography Science, Nanjing Normal University, Nanjing 210023, China, [3]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China, [4]School of Geography Science, Xinyang Normal University, Xinyang 464000, Henan Province, China
相关基金：Foundation item： Under the auspices of Special Financial Grant and General Financial Grant from the China Postdoctoral Science Foundation （No. 2015T80127, 2014M561040）, National Natural Science Foundation of China （No. 41371172, 41401171, 41471143）, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions （No. 164320H101）

关键词：人工神经网络模型, 城市土地利用, 土地集约利用, 利用评价, 南京市, 中国, 土地利用强度, 多因素综合评价, urban land, intensive use, functional area, artificial neural network （ANN） model, Nanjing City

中文摘要：

在这份报纸，人工的神经网络(ANN ) 模型被用来在南京城市里评估集中的城市的陆地使用的度，中国。ANN 模型的构造和申请考虑了城市的陆地使用的全面、空间、复杂的性质。通过样品区域的集中的陆地使用的度的初步的计算，代表性的样品区域选择并且用背繁殖训练的神经网络模型，每个评估单位的集中的陆地使用水平最后在学习区域被决定。当时，结果证明方法罐头有效地改正模型本身的限制和理想的价值和重量的决心引起的错误 multifactor 全面评估独自被使用。ANN 模型能使评估结果更客观、实际。评估结果从到圆周的城市的区域和工业功能的区域有的核心显示出减少的陆地使用紧张的一个趋势相对低的陆地使用紧张与另外的功能的区域相比。基于评估结果，一些建议被提出例如转变城市的空间扩大的模式，加强集成和陆地的潜在的利用在城市布满建筑物区域，并且加强保护的区域的构造紧张的控制。

英文摘要：

In this paper, the artificial neural network （ANN） model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.

同期刊论文项目