What does this research mean for the field?

Built-up intensity, bare land index, night-time light intensity, and population density are the dominant positive drivers of Surface Urban Heat Island (SUHI) intensity, with their influence exhibiting significant spatial heterogeneity that necessitates location-specific mitigation. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To analyze the key drivers and spatial patterns influencing surface urban heat island (SUHI) intensity in rapidly expanding metropolitan environments using machine learning and explainable artificial intelligence.

August 22, 2026Computational Urban ScienceOpen Access

Predicting surface urban heat island intensity and its drivers using machine learning and spatial XAI for achieving SDG-11

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Authors

SShahfahad STSwapan Talukdar MNMohd Waseem Naikoo

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Overview

Spatial machine learning analysis reveals built-up density drives surface urban heat island expansion in metropolitan areas, highlighting the need for location-specific mitigation.

Key Points

To analyze the key drivers and spatial patterns influencing surface urban heat island (SUHI) intensity in rapidly expanding metropolitan environments using machine learning and explainable artificial intelligence.
Trained and evaluated machine learning models (Random Forest, Gradient Boosting, XGBoost, LightGBM) integrated with Google Earth Engine to model SUHI intensity.
Conducted multi-stage feature selection using correlation screening and variance inflation factor (VIF) analysis, evaluated via spatial cross-validation and bootstrap uncertainty analysis.
Applied graphical and spatial explainable artificial intelligence (XAI) frameworks to interpret driver contributions and map heat hotspots across 2002 to 2024.
LightGBM achieved the best overall predictive performance (test R² = 0.86, RMSE = 1.49 °C), followed closely by XGBoost (test R² = 0.86, RMSE = 1.52 °C), with strong spatial generalization (CV R² = 0.77–0.78).
Explainable AI identified built-up index (NDBI), bare land index, night-time light, and population density as dominant positive drivers, while low-intensity SUHI areas declined from 13.69% in 2002 to 6.31% in 2024 as high-intensity zones expanded.
Hotspots concentrated in densely urbanized and industrial sectors, whereas coastal areas, water bodies, and vegetated zones consistently moderated heat island intensity.

Cite This Study

Shahfahad et al. (2026) studied this question.

synapsesocial.com/papers/6a895eaeca7ade938187cb9d https://doi.org/10.1007/s43762-026-00291-4

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August 22, 2026Computational Urban ScienceOpen Access

Predicting surface urban heat island intensity and its drivers using machine learning and spatial XAI for achieving SDG-11

View Full Paper

Ask AI

Bookmark

Authors

SShahfahad STSwapan Talukdar MNMohd Waseem Naikoo

Discussion

Member takes

Overview

Spatial machine learning analysis reveals built-up density drives surface urban heat island expansion in metropolitan areas, highlighting the need for location-specific mitigation.

Key Points

To analyze the key drivers and spatial patterns influencing surface urban heat island (SUHI) intensity in rapidly expanding metropolitan environments using machine learning and explainable artificial intelligence.
Trained and evaluated machine learning models (Random Forest, Gradient Boosting, XGBoost, LightGBM) integrated with Google Earth Engine to model SUHI intensity.
Conducted multi-stage feature selection using correlation screening and variance inflation factor (VIF) analysis, evaluated via spatial cross-validation and bootstrap uncertainty analysis.
Applied graphical and spatial explainable artificial intelligence (XAI) frameworks to interpret driver contributions and map heat hotspots across 2002 to 2024.
LightGBM achieved the best overall predictive performance (test R² = 0.86, RMSE = 1.49 °C), followed closely by XGBoost (test R² = 0.86, RMSE = 1.52 °C), with strong spatial generalization (CV R² = 0.77–0.78).
Explainable AI identified built-up index (NDBI), bare land index, night-time light, and population density as dominant positive drivers, while low-intensity SUHI areas declined from 13.69% in 2002 to 6.31% in 2024 as high-intensity zones expanded.
Hotspots concentrated in densely urbanized and industrial sectors, whereas coastal areas, water bodies, and vegetated zones consistently moderated heat island intensity.

Cite This Study

Shahfahad et al. (2026) studied this question.

synapsesocial.com/papers/6a895eaeca7ade938187cb9d https://doi.org/10.1007/s43762-026-00291-4

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