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Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China

QIAO Weifeng,  GAO Junbo,  LIU Yansui,  QIN Yueheng,  LU Cheng,  JI Qingqing

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文章导航 > Chinese Geographical Science  > 2017 >  27(5): 735-746
QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. 中国地理科学, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
引用本文: QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. 中国地理科学, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
shu
QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. Chinese Geographical Science, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
Citation: QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. Chinese Geographical Science, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
shu

Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China

doi: 10.1007/s11769-017-0905-7
  • 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

基金项目: 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)
详细信息
    通讯作者:

    LIU Yansui,E-mail:liuys@igsnrr.ac.cn

Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China

  • 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

Funds: 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)
More Information
    Corresponding author: LIU Yansui,E-mail:liuys@igsnrr.ac.cn

  • 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.
    Abstract: 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.
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出版历程
  • 收稿日期:  2017-01-09
  • 修回日期:  2017-05-04
  • 刊出日期:  2017-10-27

Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China

doi: 10.1007/s11769-017-0905-7
  • 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
    • 基金项目:  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)
    通讯作者: LIU Yansui,E-mail:liuys@igsnrr.ac.cn
  • 收稿日期: 2017-01-09
  • 修回日期: 2017-05-04
  • 刊出日期: 2017-10-27

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.

English Abstract

QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. 中国地理科学, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
引用本文: QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. 中国地理科学, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
shu
QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. Chinese Geographical Science, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
Citation: QIAO Weifeng, GAO Junbo, LIU Yansui, QIN Yueheng, LU Cheng, JI Qingqing. Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City, China[J]. Chinese Geographical Science, 2017, 27(5): 735-746. doi: 10.1007/s11769-017-0905-7
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