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Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine

Author

Listed:
  • Yang Zhang

    (College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China)

  • Ping Jiang

    (College of Resources and Environmental Science, Wuhan University, Wuhan 430079, China)

  • Hongyan Zhang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China)

  • Peng Cheng

    (College of Public Administration, Guangxi University, Nanning 530004, China)

Abstract

Thermal infrared remote sensing has become one of the main technology methods used for urban heat island research. When applying urban land surface temperature inversion of the thermal infrared band, problems with intensity level division arise because the method is subjective. However, this method is one of the few that performs heat island intensity level identification. This paper will build an intensity level identifier for an urban heat island, by using weak supervision and thought-based learning in an improved, restricted Boltzmann machine (RBM) model. The identifier automatically initializes the annotation and optimizes the model parameters sequentially until the target identifier is completed. The algorithm needs very little information about the weak labeling of the target training sample and generates an urban heat island intensity spatial distribution map. This study can provide reliable decision-making support for urban ecological planning and effective protection of urban ecological security. The experimental results showed the following: (1) The heat island effect in Wuhan is existent and intense. Heat island areas are widely distributed. The largest heat island area is in Wuhan, followed by the sub-green island. The total area encompassed by heat island and strong island levels accounts for 54.16% of the land in Wuhan. (2) Partially based on improved RBM identification, this method meets the research demands of determining the spatial distribution characteristics of the internal heat island effect; its identification accuracy is superior to that of comparable methods.

Suggested Citation

  • Yang Zhang & Ping Jiang & Hongyan Zhang & Peng Cheng, 2018. "Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine," IJERPH, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:2:p:186-:d:128315
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    References listed on IDEAS

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    1. Simon Gosling & Jason Lowe & Glenn McGregor & Mark Pelling & Bruce Malamud, 2009. "Associations between elevated atmospheric temperature and human mortality: a critical review of the literature," Climatic Change, Springer, vol. 92(3), pages 299-341, February.
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    1. Chenying Li & Tiantian Zhang & Xi Wang & Zefeng Lian, 2022. "Site Selection of Urban Parks Based on Fuzzy-Analytic Hierarchy Process (F-AHP): A Case Study of Nanjing, China," IJERPH, MDPI, vol. 19(20), pages 1-27, October.

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