EventsThe 8th International Electronic Conference on Water Sciences
Published
This submission belongs to the session S5. Numerical and Experimental Methods, Data Analyses, Digital Twin, IoT Machine Learning and AI in Water Sciences of the event The 8th International Electronic Conference on Water Sciences
Published date
11 Oct, 2024
Academic Editor
author-avatarJunye Wang
Citation
Hooman Razavi, Ali Asgary, Hossein Bonakdari, Amirhossein Mostofi, Omid Titidezh, Generative AI-Aided Digital Twin for Urban Flood Risk Assessment: Challenges and Opportunities, in Proceedings of The 8th International Electronic Conference on Water Sciences, 14 October–16 October 2024, MDPI: Basel, Switzerland
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Generative AI-Aided Digital Twin for Urban Flood Risk Assessment: Challenges and Opportunities

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Omid Titidezh 5
1. Tecnologico de Monterrey, Mexico, Mexico
2. School of Administrative Studies, Faculty of Liberal Arts & Professional Studies, York University, Toronto, Canada, Canada
3. Department of Civil Engineering, University of Ottawa, Ottawa, Canada, Canada
4. Business School - Management, Technology & Organisation, Auckland University of Technology, New Zealand, New Zealand
5. School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, United Kingdom, UK
Abstract

Floods are the most frequent and destructive disasters, causing widespread destruction, A significant loss of life, and substantial economic impacts globally. Climate change exacerbates urban flood risks by altering precipitation patterns, increasing the frequency of extreme weather events, and raising sea levels. These escalating risks necessitate innovative solutions for effective urban flood management. Digital Twin (DT) technology offers a promising solution by providing virtual models and data-driven simulations that enhance urban landscape management, enabling timely flood warnings, evacuation planning, and property protection. Integrating Generative Artificial Intelligence (GenAI) further elevates digital twins by enhancing predictive capabilities and creating more realistic and interactive scenarios. Utilizing generative algorithms, digital twins can generate synthetic data to simulate a wide range of potential outcomes, improving the accuracy of modeling complex systems and forecasting variations and challenges. Moreover, GenAI improves the precision and reliability of digital twins in representing real-world environments through high-fidelity simulations. This study synthesizes findings from the literature review to develop a conceptual framework for elucidating the synergies between generative AI and digital twins in the context of urban flood risk assessment. It begins with an introduction to the integration of generative AI and digital twins for simulating flood scenarios. The paper then delves into the fundamentals of generative AI, discussing its principles, applications, and successful implementations across various domains, particularly in urban flood risk assessment. Subsequent sections examine the evolution of digital twins and their critical role in assessing, predicting, and mitigating flood risks. The study further investigates the intersection of generative AI and digital twins, highlighting the enhanced simulation capabilities provided by GenAI. Concluding with an in-depth analysis of specific applications of GenAI-enhanced digital twins in flood risk assessment, the study anticipates future challenges and advancements, emphasizing emerging trends and potential development opportunities.

Keywords
Generative Artificial Intelligence
Big Data Analytics
Disaster Mitigation
Digital Twin
Flood
Risk Assessment
Scenario Simulation
Synthetic Data
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