Artificial Intelligence (AI) for Disaster-Resilient Urban Planning: A Comprehensive Data-Driven Review Framework

Harnessing big data for enhanced equitable disaster resilience: Integrating technology and community-centric approaches

Artificial intelligence (AI) stands at the forefront of transforming emergency management, offering unprecedented capabilities in disaster preparedness and response. Recent implementations demonstrate this shift from reactive to proactive approaches, particularly through flood prediction algorithms and maritime search-and-rescue optimization systems that integrate real-time vessel locations and weather data. However, the current landscape reveals a critical challenge: The opacity of AI systems creates a significant trust deficit among emergency responders and communities. Research findings paint a concerning picture of this transparency gap. A comprehensive survey of emergency management AI systems reveals striking statistics: 68 percent lack adequate documentation of their data sources, while 42 percent fail to provide clear justifications for their recommendations. This “black box” phenomenon carries serious implications, particularly when flood prediction models disproportionately affect vulnerable populations or when opaque decision-making processes lead to suboptimal resource allocation during critical rescue operations. Analysis of real-world applications in flood preparedness and search-and-rescue operations exposes systematic communication deficiencies within these essential emergency response frameworks. The research examines how varying levels of AI transparency directly influence emergency responders’ decision-making during crises, exploring the delicate balance between operational openness and security considerations. These findings highlight an urgent need for robust oversight mechanisms and context-specific transparency protocols to ensure ethical AI deployment in emergency management. The evidence points toward a clear solution: developing human-centric approaches that enhance rather than replace human capabilities in emergency response. This strategy requires establishing tailored transparency guidelines and monitoring systems that address current challenges while facilitating effective AI integration. By prioritizing both technological advancement and human oversight, emergency management systems can better serve their critical public safety mission.

Assessing Sustainability and Inclusivity in Urban Planning Through Artificial Intelligence: A Case Study of George Town, Chennai

A Comprehensive Disaster Risk Analysis Model With GeoAI

Abstract. Natural and technological disasters continue to threaten communities, infrastructure, and the environment, and the cascading nature of contemporary risks complicates their assessment. This study presents a disaster risk analysis model coupling Geographic Information Systems (GIS), ensemble machine learning, and AI-driven interpretation tools, collectively termed GeoAI, to support multi-hazard risk assessment, resilience planning, and citizen-oriented risk communication. Rather than building isolated models per hazard, the framework applies a single, modular pipeline consistently across flood, wildfire, earthquake, landslide, drought, and urban heat island hazards. Hazard, vulnerability, and exposure layers are derived from open geospatial datasets and processed in QGIS, after which Random Forest and XGBoost classifiers generate hazard and vulnerability maps validated using AUC-ROC, F1-score, and Cohen's Kappa. A distinctive component is an AI agent built on Large Language Models (LLMs) and the Model Context Protocol (MCP), which queries structured risk databases and produces region-specific narrative risk reports in natural language. Outputs are delivered through a web-based platform with an MCP-connected chatbot, lowering the barrier to understanding complex risk information for experts and the public. The model was tested in Türkiye's Marmara Region, which is important due to its dense population, heavy industry, and earthquake risk along the North Anatolian Fault. The pilot showed good results: XGBoost performed better than the baseline models, and the LLM-based interpretation layer gave clear, well-grounded outputs. The framework offers a scalable, open, interoperable approach linking spatial analytics, machine learning, and generative AI for evidence-based disaster risk reduction.

Conceptual Advances in Predictive Intelligence Models for Humanitarian and Disaster Response Supply Chain Resilience

Humanitarian and disaster response supply chains operate under extreme uncertainty, time pressure, and resource constraints, where delays or misallocations directly translate into human suffering and loss of life. In recent years, predictive intelligence models have emerged as critical enablers for enhancing supply chain resilience by improving anticipatory decision-making, situational awareness, and adaptive coordination across complex humanitarian networks. This review examines conceptual advances in predictive intelligence models applied to humanitarian and disaster response supply chains, with emphasis on their theoretical foundations, methodological evolution, and resilience-oriented capabilities. The paper synthesizes developments across data-driven forecasting, probabilistic risk modeling, machine learning, and hybrid human–AI decision frameworks, highlighting how these approaches support demand anticipation, disruption prediction, inventory pre-positioning, and logistics network reconfiguration. Particular attention is given to the integration of real-time data streams from remote sensing, social media, Internet of Things devices, and institutional reporting systems, as well as the role of explainability and trust in high-stakes humanitarian contexts. The review also discusses persistent challenges, including data sparsity, ethical constraints, model transferability across disaster types and regions, and governance issues related to inter-agency coordination. By organizing the literature around resilience dimensions—robustness, adaptability, and recoverability—the paper offers a unifying conceptual lens for evaluating predictive intelligence models beyond pure accuracy metrics. The study concludes by identifying research gaps and proposing future directions, including human-centered predictive systems, federated and privacy-preserving learning, and policy-aligned intelligence architectures. Overall, the review provides a structured foundation for researchers, practitioners, and policymakers seeking to leverage predictive intelligence to strengthen humanitarian supply chain resilience in increasingly volatile disaster environments.

Smart technologies in enhancing flood resilience in coastal African cities

Coastal African cities face growing flood risks due to climate change, rapid urbanization, and inadequate infrastructure. This study examines flood risk and resilience strategies in four representative coastal cities, Accra (Ghana), Cape Town (South Africa), Mombasa (Kenya), and Tunis (Tunisia), and evaluates the role of smart, data-driven technologies within smart city governance frameworks. A purposive case study approach is combined with a descriptive literature review drawing on peer-reviewed articles, policy reports, and technical documents published between 2000 and 2024. The collected literature is analyzed using a comparative thematic approach across key disaster risk management phases, including risk assessment, early warning, preparedness, and governance. Results indicate that while all four cities are adopting smart technologies for flood resilience, their applications vary by institutional capacity and resource availability. Accra emphasizes community-based early warning systems and GIS-supported decision-making; Cape Town demonstrates advanced real-time monitoring and predictive modeling but faces challenges in public engagement; Mombasa is piloting low-cost IoT-based solutions with constraints in data integration; and Tunis is integrating smart technologies into urban planning, though scaling remains limited. Despite these efforts, common barriers persist, including funding limitations, data gaps, and fragmented governance. The study provides comparative insights to support the design of context-specific, technology-enabled flood resilience strategies in African coastal cities.

Artificial Intelligence and Big Data in Disaster Risk Management from a Socio-Legal Perspective

To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.