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  1. Home
  3. From Data To Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis And Interpretable Machine Learning.
  1. Home
  3. From Data To Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis And Interpretable Machine Learning.

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From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine

Nhat-Duc Hoang1,2, Van-Duc Tran2,3, Thanh-Canh Huynh1,2

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

Sensors (Basel, Switzerland)
|February 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed a machine learning model to predict land surface temperature (LST) in Da Nang, Vietnam. Urban density and greenspace density were found to be the most significant factors influencing LST.

Keywords:
Shapley additive explanationsbuilt environmentinterpretable machine learningland surface temperatureurban heat

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Urban Planning

Background:

  • Urban heat islands significantly impact city environments.
  • Accurate modeling of land surface temperature (LST) is crucial for understanding urban heat stress.
  • Da Nang, Vietnam, faces increasing urban development and associated thermal challenges.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting urban land surface temperature (LST) in Da Nang.
  • To identify key factors influencing spatial LST variations.
  • To provide insights for sustainable urban planning and heat stress mitigation.

Main Methods:

  • Employed Light Gradient Boosting Machine (LightGBM), Support Vector Machine, Random Forest, and Deep Neural Network.
  • Utilized remote sensing data from 2014, 2019, and 2024 for model training and validation.
  • Applied Shapley Additive Explanations to interpret model results and identify influential factors.

    • Main Results:

    • LightGBM demonstrated superior performance compared to other benchmark machine learning methods.
    • Urban density and greenspace density were consistently identified as the most influential factors affecting LST.
    • Achieved high R-squared values (0.85, 0.92, 0.91) for 2014, 2019, and 2024, respectively.

    Conclusions:

    • Machine learning, particularly LightGBM, effectively models spatial LST variations in urban areas.
    • Urban form characteristics, specifically density and green space, are critical drivers of LST.
    • Findings support evidence-based urban planning for mitigating heat stress and enhancing urban resilience.