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Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning.

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Area of Science:

  • Urban Climatology
  • Environmental Science
  • Remote Sensing

Background:

  • Urbanization globally leads to complex interactions between citizens and urban temperatures.
  • Existing research often focuses on broad, linear relationships between urban features and temperature.
  • A nuanced understanding of specific urban feature impacts on the thermal environment is lacking.

Purpose of the Study:

  • To decouple the urban thermal environment using advanced analytical methods.
  • To identify the specific driving effects of varying urban feature ranges on temperature.
  • To determine an optimal observation buffer zone for analyzing urban surface temperature.

Main Methods:

  • Utilized Landsat satellite data for urban thermal environment analysis.
  • Employed Bayesian optimization ensemble learning to model complex relationships.
  • Applied Shapley value to quantify the contribution of individual urban features.

Main Results:

  • Identified a 2 km observation buffer zone as optimal for this dataset.
  • Found ecological factors exert a greater influence on urban temperature than urban morphology.
  • Quantified seasonal cooling effects of vegetation coverage and building height, noting specific thresholds for maximum impact.

Conclusions:

  • Ecological factors, particularly vegetation, play a critical role in mitigating urban heat.
  • Building height demonstrates seasonal variations in its cooling effect.
  • Findings provide data-driven insights for urban planners to optimize built environments for thermal comfort.