Data-driven assessment of industrial influences on urban heat index dynamics across Telangana, India

Urban heat stress is an escalating environmental risk in rapidly industrialising regions of India, where land-use transformation and industrial expansion interact with background climatic warming. This study examines the spatio-temporal evolution of land surface temperature (LST) and the Urban Heat Index (UHI_index) across Telangana, India, over the period 2003-2023, with specific emphasis on the thermal influence of major industrial zones. Multi-temporal Landsat imagery, land-use information, and meteorological variables were analysed within a geospatial data analytics framework to quantify UHI intensity, temporal trends, and industrial attribution. Results indicate systematic amplification of UHI_index within industrial areas relative to non-industrial zones. Mean UHI_index in industrial regions increased from approximately 1.35 in 2003 to 2.70 in 2023, whereas non-industrial areas exhibited a lower increase from 1.00 to 1.82. Machine learning models demonstrated strong predictive performance (R2 ≈ 0.87; RMSE ≈ 0.26), identifying industrial land use, built-up density, and near-surface air temperature as dominant drivers. Monte Carlo uncertainty analysis (n = 100) indicates stable regional inference (± 0.18 UHI_index). The findings provide quantitative evidence of industrial contributions to urban thermal intensification and offer a reproducible framework for industrial heat mitigation planning in rapidly developing Indian states.

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Publication Details

Journal
Scientific Reports
Published
2026-05-01
DOI
https://doi.org/10.1038/s41598-026-45874-7
Primary Topic
Urban Heat Island Mitigation
Type
article
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article

Data-driven assessment of industrial influences on urban heat index dynamics across Telangana, India

Vinay Kumar Gaddam, Shankar Karuppannan, Tan Kuan Tak, Sivaneasan Bala Krishnan et al.
Scientific Reports
Urban Heat Island Mitigation
article

Data-driven assessment of industrial influences on urban heat index dynamics across Telangana, India

Vinay Kumar Gaddam, Shankar Karuppannan, Tan Kuan Tak, Sivaneasan Bala Krishnan, Bhogayya Naidu
article en

Abstract

Urban heat stress is an escalating environmental risk in rapidly industrialising regions of India, where land-use transformation and industrial expansion interact with background climatic warming. This study examines the spatio-temporal evolution of land surface temperature (LST) and the Urban Heat Index (UHI_index) across Telangana, India, over the period 2003-2023, with specific emphasis on the thermal influence of major industrial zones. Multi-temporal Landsat imagery, land-use information, and meteorological variables were analysed within a geospatial data analytics framework to quantify UHI intensity, temporal trends, and industrial attribution. Results indicate systematic amplification of UHI_index within industrial areas relative to non-industrial zones. Mean UHI_index in industrial regions increased from approximately 1.35 in 2003 to 2.70 in 2023, whereas non-industrial areas exhibited a lower increase from 1.00 to 1.82. Machine learning models demonstrated strong predictive performance (R2 ≈ 0.87; RMSE ≈ 0.26), identifying industrial land use, built-up density, and near-surface air temperature as dominant drivers. Monte Carlo uncertainty analysis (n = 100) indicates stable regional inference (± 0.18 UHI_index). The findings provide quantitative evidence of industrial contributions to urban thermal intensification and offer a reproducible framework for industrial heat mitigation planning in rapidly developing Indian states.

Scientific Reports
Siddhartha Medical College (IN), Chaitanya Bharathi Institute of Technology (IN), Singapore Institute of Technology (SG), Saveetha University (IN), Adama Science and Technology University (ET)
Sustainable cities and communities
Openalex Percentile: Top 12%
Urban Heat Island Mitigation
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Data-driven assessment of industrial influences on urban heat index dynamics across Telangana, India — Vinay Kumar Gaddam, Shankar Karuppannan, et al. · Scientific Reports (2026) | TGRS Research Map | TGRS