AI-Driven Climate Analysis for Sustainable Urban Planning

  • Authors

    • Thomas Fischer Senior Manager, Bosch, Germany. Author
    • Anna Schmidt Product Director, BMW Group, Germany. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2022PII5Y2L

    Published 07-03-2022

  • Artificial Intelligence, Climate Modeling, Sustainable Cities, Urban Planning, Machine Learning, Smart Cities

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    T. Fischer and A. Schmidt, “AI-Driven Climate Analysis for Sustainable Urban Planning”, ijmiet, vol. 5, no. 2, pp. 01–16, Jul. 2022, doi: 10.67228/30715628/IJMIET-2022PII5Y2L.

  • Abstract

    The challenges are urban heat islands, air pollution, flooding, among others that have a higher intensity due to rapid urbanization. Traditional urban planning methods are based on very conservative models with reference to the past and lack the ability to dynamically respond to changing climatic conditions. The presented paper outlines an AI-based framework of climate analysis that is aimed at facilitating sustainable urban planning by using predictive analytics, spatial intelligence, and decision optimization. The suggested framework will be able to access precise climate impact evaluation at small urban scales by merging machine learning, deep learning, remote sensing, and geospatial data. The paper discusses the possibilities of AI models to predict temperature changes, precipitation ranges, air quality trends and energy demand based on various urban development conditions. A modular approach that considers data acquisition, preprocessing, feature engineering, training of the models, and policy-oriented decision support is established. The combination of experimental findings shows that the process of prediction and the planning process is better than when using the traditional forms of statistical processes. The conclusions affirm that AI-based climate analytics have the potential to provide a great boost to resilience and sustainability as well as support evidence-based decision-making in urban development.

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