Machine Learning Techniques for Urban Resilience: A Systematic Review and Future Directions

Urban resilience has become a critical paradigm for cities facing escalating threats from climate change, rapid urbanization, and infrastructure vulnerabilities. This paper presents a rigorous systematic review of 56 peer-reviewed studies (2015-2023) examining machine learning (ML) applications in urban resilience planning and disaster management. Our analysis reveals three dominant themes: (1) ML's growing role in predictive modeling of disasters through techniques like LSTM networks and CNNs, (2) emerging applications in infrastructure interdependency analysis using graph neural networks, and (3) innovative approaches to resource allocation through reinforcement learning. The review identifies significant gaps in geographic representation, with 78% of studies focused on developed nations, while vulnerable regions in the Global South remain understudied. We also highlight critical challenges in model interpretability, with only 15% of studies incorporating explainability tools like SHAP or LIME. The paper contributes a novel taxonomy classifying 12 major ML techniques by their urban resilience applications, computational requirements, and ethical considerations. Furthermore, we propose a framework for integrating ML into urban governance that emphasizes transfer learning for data-scarce regions and federated learning for privacy preservation. This work provides both researchers and policymakers with actionable insights for developing more equitable, robust, and transparent ML solutions for urban resilience challenges.