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<p>Rapid urbanization and climate change have exacerbated heat stress in metropolitan regions like Delhi, India. This study investigates the spatio-temporal dynamics of Wet-Bulb Temperature (WBT) and Land Surface Temperature (LST) from 2005 to 2024, and projects future WBT trends using a Long Short-Term Memory (LSTM) model. The novelty of this research lies in integrating satellite-based climate data with machine learning algorithms for early warning systems and urban resilience planning. The study reveals a significant rise in WBT over the past two decades, with projections indicating values exceeding 35&#xa0;°C during extreme heat events by 2030, especially in densely built-up zones. Using LANDSAT imagery and urban expansion data, a strong positive correlation was observed between urbanization and elevated WBT levels. A Pearson correlation analysis revealed prolonged thermal stress through the strong associations between May LST values from recent years, particularly between the years 2023 and 2024 (<i>r</i> = 0.74) and 2015 and 2023 (<i>r</i> = 0.71). The thermal patterns experienced change because of increased Urban Heat Island (UHI) effects, as shown in the early-year correlations between 2005 and 2021 (<i>r</i> = 0.20). Spatial measurements verified that populated city centers recorded elevated WBT and LST data, which reflect the impact of urbanization and damaged vegetated areas. Real-time monitoring combined with ML-driven alerts and sustainable planning interventions needs immediate implementation, according to WBT forecasting results produced by the LSTM model. The study provides critical insights for policymakers to formulate evidence-based heat mitigation strategies, aiming to safeguard public health and labor productivity under future climate scenarios.</p> Graphical Abstract <p>The graphical abstract demonstrates the temperature change effects of global warming by showing NASA-POWER data from 2005 through 2024 to visualize temperature trends across space and time. The analyzed data show that urbanized territories have experienced substantial growth in both Land Surface Temperatures (LST) and Wet-Bulb Temperatures (WBT). The urban areas illustrate higher temperature values due to their increased construction and diminished vegetation, which intensifies the Urban Heat Island (UHI) effect. The change from natural environments to concrete and asphalt surfaces results in holding heat amounts that disrupt natural climate regulation systems. The data projection based on Long Short-Term Memory (LSTM) network models predicts that LST and WBT temperatures will continue to increase until 2030. Climate change and urbanization effects continue to play a role in the environment, resulting in more heat events that become more severe. The combination of temperature and humidity, known as WBT, has increased to significant levels because it prevents effective body cooling, thus posing severe health threats to at-risk population groups. The clear illustration demonstrates the pressing requirement for climate-adaptation solutions since urban areas are expanding at a fast pace. The plan requires the implementation of cooling strategies that combine green cities with resilient urban development practices and sustainable land use practices. Strategies to manage the UHI effect will help cities lessen heat stress hazards and defend public health while creating sustainable living areas under climate change conditions.</p> <p></p>

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Machine Learning-Based Forecasting of Wet-Bulb Temperature and Two-Decade Urban Climate Shifts

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Aryan Tyagi,
  • Sagar Tomar,
  • Alisha Raut,
  • Kishor S. Kulkarni,
  • Shilpa Sharma,
  • Tarig Ali,
  • Jerry Wayne Nave,
  • Rabin Chakrabortty

摘要

Rapid urbanization and climate change have exacerbated heat stress in metropolitan regions like Delhi, India. This study investigates the spatio-temporal dynamics of Wet-Bulb Temperature (WBT) and Land Surface Temperature (LST) from 2005 to 2024, and projects future WBT trends using a Long Short-Term Memory (LSTM) model. The novelty of this research lies in integrating satellite-based climate data with machine learning algorithms for early warning systems and urban resilience planning. The study reveals a significant rise in WBT over the past two decades, with projections indicating values exceeding 35 °C during extreme heat events by 2030, especially in densely built-up zones. Using LANDSAT imagery and urban expansion data, a strong positive correlation was observed between urbanization and elevated WBT levels. A Pearson correlation analysis revealed prolonged thermal stress through the strong associations between May LST values from recent years, particularly between the years 2023 and 2024 (r = 0.74) and 2015 and 2023 (r = 0.71). The thermal patterns experienced change because of increased Urban Heat Island (UHI) effects, as shown in the early-year correlations between 2005 and 2021 (r = 0.20). Spatial measurements verified that populated city centers recorded elevated WBT and LST data, which reflect the impact of urbanization and damaged vegetated areas. Real-time monitoring combined with ML-driven alerts and sustainable planning interventions needs immediate implementation, according to WBT forecasting results produced by the LSTM model. The study provides critical insights for policymakers to formulate evidence-based heat mitigation strategies, aiming to safeguard public health and labor productivity under future climate scenarios.

Graphical Abstract

The graphical abstract demonstrates the temperature change effects of global warming by showing NASA-POWER data from 2005 through 2024 to visualize temperature trends across space and time. The analyzed data show that urbanized territories have experienced substantial growth in both Land Surface Temperatures (LST) and Wet-Bulb Temperatures (WBT). The urban areas illustrate higher temperature values due to their increased construction and diminished vegetation, which intensifies the Urban Heat Island (UHI) effect. The change from natural environments to concrete and asphalt surfaces results in holding heat amounts that disrupt natural climate regulation systems. The data projection based on Long Short-Term Memory (LSTM) network models predicts that LST and WBT temperatures will continue to increase until 2030. Climate change and urbanization effects continue to play a role in the environment, resulting in more heat events that become more severe. The combination of temperature and humidity, known as WBT, has increased to significant levels because it prevents effective body cooling, thus posing severe health threats to at-risk population groups. The clear illustration demonstrates the pressing requirement for climate-adaptation solutions since urban areas are expanding at a fast pace. The plan requires the implementation of cooling strategies that combine green cities with resilient urban development practices and sustainable land use practices. Strategies to manage the UHI effect will help cities lessen heat stress hazards and defend public health while creating sustainable living areas under climate change conditions.