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Machine Learning Approaches for Geospatial Modeling of Urban Land Surface Temperature: Assessing Geographical

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  • 1Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.

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Summary
This summary is machine-generated.

This study models urban heat in Vietnam using machine learning, identifying urban compactness as key to predicting land surface temperature (LST). Findings aid sustainable urban planning and heat stress reduction.

Keywords:
Landsat 8Quang Ngai ProvinceSHAPSentinel-2Vietnamgeographical compactnesshighland regionland surface temperaturemachine learningurban morphology

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Geospatial Analysis

Background:

  • Limited research exists on urban heat stress in Quang Ngai Province, Vietnam.
  • Understanding urban heat island (UHI) effects is crucial for sustainable development.

Purpose of the Study:

  • To develop a data-driven framework for modeling urban heat distribution.
  • To identify key urban features influencing land surface temperature (LST).

Main Methods:

  • Utilized Category Boosting (CatBoost) and Convolutional Neural Network (CNN) for LST prediction.
  • Incorporated topographical, land use/land cover, and urban morphological features.
  • Employed Shapley Additive Explanations (SHAP) for feature importance analysis.

Main Results:

  • Urban compactness metrics significantly improved LST prediction accuracy.
  • CatBoost model explained 89% of the variance in LST.
  • Key factors influencing LST include built-up density, bare land density, proximity to rivers, and green space density.

Conclusions:

  • The proposed framework effectively models urban heat characteristics in the study region.
  • Findings provide crucial insights for urban heat stress alleviation strategies.
  • Results support evidence-based sustainable urban planning in Vietnam.