Machine Learning for Urban Heat Island Mitigation and Sustainable City Planning
Abstract
Rapid urbanization has led to the intensification of the urban heat island (UHI) effect, causing environmental and public health challenges. This paper explores machine learning techniques for UHI mitigation, including predictive modeling of temperature variations, AI-driven green infrastructure planning, and optimization of urban cooling strategies. Remote sensing, deep learning, and geospatial analysis are utilized to assess heat patterns and recommend sustainable urban designs. Case studies highlight AI-driven interventions such as smart shading systems, green roofs, and reflective materials to reduce heat retention and promote climate-resilient cities.
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