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.

  • References

    [1] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    [2] Vapnik, V. N. (1995). The nature of statistical learning theory. Springer-Verlag, New York.

    [3] Abbot, J., & Marohasy, J. (2017). Application of artificial neural networks to rainfall forecasting. Atmospheric Research, 196, 130–141.

    [4] Reichstein, M., Camps-Valls, G., Stevens, B., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204.

    [5] Shi, X., Chen, Z., Wang, H., et al. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, 28.

    [6] Ham, Y. G., Kim, J. H., & Luo, J. J. (2019). Deep learning for multi-year ENSO forecasts. Nature, 573, 568–572.

    [7] Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384.

    [8] Weng, Q., Fu, P., & Gao, F. (2014). Generating daily land surface temperature at Landsat resolution. Remote Sensing of Environment, 140, 199–212.

    [9] Li, X., Zhou, Y., Asrar, G. R., et al. (2018). Response of vegetation greenness to urbanization. Remote Sensing of Environment, 210, 1–15.

    [10] Zhang, Y., Li, D., & Wang, X. (2020). Urban heat island mapping using deep learning and remote sensing data. Sustainable Cities and Society, 53, 101942.

    [11] Kumar, P., Skouloudis, A. N., Bell, M., et al. (2017). Real-time sensors for indoor air monitoring. Science of the Total Environment, 560–561, 150–165.

    [12] Zheng, Y., Liu, F., & Hsieh, H. P. (2013). U-Air: When urban air quality inference meets big data. Proceedings of the 19th ACM SIGKDD, 1436–1444.

    [13] Kong, W., Dong, Z. Y., Jia, Y., et al. (2017). Short-term residential load forecasting based on LSTM networks. IEEE Transactions on Smart Grid, 10(1), 841–851.

    [14] Willard, J., Jia, X., Xu, S., et al. (2020). Integrating physics-based modeling with machine learning. Nature Machine Intelligence, 2(12), 732–743.

    [15] Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6.

  • Downloads

Similar Articles

61-70 of 74

You may also start an advanced similarity search for this article.