Transforming Post-Disaster Risk Management: A Comprehensive Framework for Digital Twinning with AI and Machine Learning

Digital Twins (DT) are dynamic digital representations of physical entities ranging from individual systems to entire cities. They leverage real-time data to create accurate models and simulations, offering significant potential for post-disaster risk management (PRM) applications. However, the integration of DT into PRM is still in its infancy, with its full capabilities yet to be realized.This study introduces the Digital Post-Disaster Risk Management Twinning (DPRMT) paradigm, which aims to harness AI and ML within DT frameworks to reinforce the resilience of urban areas and communities in the face of disasters. A critical review of 335 research papers on DPRMT from reputable databases indicates that existing literature often fails to fully appreciate the dynamic and interconnected nature of disasters, typically relying on static historical data and neglecting important financial, social, and demographic factors in affected communities.We propose a tansformative DPRMT framework that encompasses six interconnected components. Entities at Risk identifies a variety of elements vulnerable during disasters, including human lives, buildings, critical infrastrucres, and social networks. Data collection and preparation employ various methods such as remote sensing, crowdsourcing, and social sensing to gather and prepare dynamic data for analysis. Data Processing leverages artificial intelligence and machine learning to validate, fuse, and analyze collected data, enhancing its accuracy and reliability. Digital Modeling encompasses diverse techniques like AI-based modeling, socio-economic modeling, and physical modeling to create computer-based representations of entities at risk, enabling in-depth analysis and prediction. Information Decoding involves comprehensive data and model analysis, integration, and visualization, delivering timely and actionable information to enhance decision-making and transparency. User Interaction and Application ensure effective communication between digital twin models and end-users through various technologies, facilitating real-time information delivery and stakeholder engagement in disaster response and recovery. This framework is designed to fill current gaps in traditional disaster recovery methods by integrating real-time, detailed, and data-driven modeling solutions, fostering improved decision-making in areas such as policy development, resilience assessment, casualty and hazard prediction, resource distribution, evacuation planning, scenario testing, and community involvement. Despite the promise of ML in improving DT capabilities for PRM such as data validation, information extraction, predictive maintenance and anomaly detection, the results show that challenges remain, including the need for high-quality and diverse data, privacy concerns, and cost-effectiveness, particularly in less developed countries. The use of remote sensing technologies, such as satellites and drones, is presented as a viable solution to overcome these challenges. These technologies supply high-quality, detailed data on buildings, infrastructure, land cover changes, and post-disaster scenarios while addressing privacy and security concerns. Nonetheless, issues with model generalization persist, necessitating training on varied disaster contexts, managing large datasets, capturing the dynamic nature of disasters, and maintaining transparency in decision-making for practical real-time application. The limitations of current ML methods, especially their time-consuming nature and the need for frequent re-training in evolving disaster scenarios, may impede their seamless integration with DT frameworks. This highlights the need to develop more efficient and rapid ML and Deep Learning models specifically designed for the unique requirements of post-disaster recovery management.