AI-Driven Climate Analysis for Sustainable Urban Planning

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India Author
    • P. K. Iyengar Scientific Computing Researcher, BARC, India Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2025PI4GV3

    Published 02-03-2025

  • Artificial Intelligence, Climate Analysis, Sustainable Urban Planning, Machine Learning, Deep Learning, Smart Cities, Remote Sensing, Geographic Information Systems, Climate Prediction, Urban Sustainability

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar and P. K. Iyengar, “AI-Driven Climate Analysis for Sustainable Urban Planning”, ijmiet, vol. 8, no. 1, pp. 01–19, Feb. 2025, doi: 10.67228/30715628/IJMIET-2025PI4GV3.

  • Abstract

    Rapid urbanization, climate change, and environmental degradation have increased the need for intelligent urban planning approaches that address complex sustainability challenges. Traditional climate assessment methods, which rely mainly on statistical models and historical observations, often fail to capture nonlinear environmental interactions and dynamic urban growth patterns. Artificial Intelligence (AI) has emerged as a transformative solution by integrating machine learning, deep learning, remote sensing, Internet of Things (IoT) sensors, and geographic information systems (GIS) to deliver accurate climate predictions and data-driven decision support. AI-based climate analysis enables continuous monitoring of key environmental parameters, including temperature, humidity, precipitation, air quality, land surface characteristics, and greenhouse gas emissions, thereby supporting resilient urban planning. This study reviews recent AI-driven climate analysis techniques and proposes an intelligent framework that integrates diverse environmental datasets with advanced predictive models. The framework aims to improve urban resilience, optimize land-use planning, strengthen disaster preparedness, promote green infrastructure, and reduce carbon emissions through informed decision-making. Furthermore, the paper examines the role of machine learning, deep neural networks, GIS, and explainable AI in urban climate applications. The findings indicate that AI-powered climate analysis significantly enhances prediction accuracy, resource optimization, and environmental sustainability while supporting adaptive strategies for the development of smart, sustainable, and climate-resilient cities.

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