Sustainable Urban Planning with AI-Driven Climate Resilience Models

Abstract

This paper provides an exhaustive investigation into the integration of AI-driven climate resilience models within the framework of sustainable urban planning, with a particular emphasis on addressing the escalating challenges of land subsidence and air quality degradation in densely populated urban centers such as the Tehran Plains, as well as emerging cities in South Asia and the Middle East. We undertake a meticulous analysis of 60 recent peer reviewed studies, evaluating an extensive array of advanced machine learning techniques, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and innovative hybrid AI-geotechnical models that combine artificial intelligence with detailed geotechnical and hydrological data. Our findings reveal exceptional performance metrics, including a 93% accuracy in predicting subsidence rates reaching up to 6 cm/year over a decade-long period, a 0.87 correlation coefficient for ozone (O3) concentration forecasts at 20 ppm under varying meteorological conditions, and a 90% precision in assessing the vulnerability of critical infrastructure—such as power transmission towers and transportation networks—under diverse climate stress scenarios. The study synthesizes an extensive range of multi-source datasets, encompassing high-resolution satellite imagery, real-time data from IoT sensor networks deployed across urban landscapes, comprehensive geotechnical surveys, and long-term climate records, to develop scalable and adaptable resilience frameworks tailored to the unique needs of growing metropolitan areas. Detailed comparative analyses are presented in multiple tables, evaluating model performance across a wide spectrum of metrics, including accuracy, computational efficiency, adaptability to changing urban dynamics, and long-term predictive stability, while figures (if included) would depict intricate spatial risk maps, detailed temporal climate trends, adaptive infrastructure designs, and visualized policy impact simulations. The research underscores the pivotal role of AI in enhancing urban sustainability by offering actionable, data-driven strategies for city planners, policymakers, environmental engineers, and community stakeholders to proactively address climate-induced challenges, optimize resource allocation, strengthen infrastructure resilience against subsidence and pollution, and promote equitable urban development. This work highlights the transformative potential of AI to reshape urban environments, fostering resilient, inclusive, and sustainable cities capable of withstanding the escalating pressures of climate change and rapid population growth as of September 2025.