11-06, 13:40–14:05 (Australia/Hobart), Main Auditorium
An RDT framework was developed using open-source tools integrating real-time IoT data. Key outcomes include an integrated spatial database, a coupled physical-digital city model, and an interactive VR interface. This approach emphasize data interoperability and real-time event triggers, enhancing flood modeling accuracy and disaster response strategies.
The adoption of Responsive Digital Twin (RDT) technology in flood modeling has faced challenges due to a lack of practical examples. Digital Twin's key differentiator lies in its integration of near real-time data analytics via IoT sensor connectivity, yet this potential remains largely untapped, revealing a critical gap in exploiting this technology fully. This research addresses this gap by developing a comprehensive RDT framework tailored for near real-time flood modeling. The framework integrates near real-time data streams, enabling advanced analytics and unlocking Digital Twin's true potential in flood modeling and disaster event monitoring. Key outcomes include an integrated back-end spatial database, a coupled physical city, and digital space model, along with a functional front-end flood interface. The research aligns with Gemini Principles, emphasizing data interoperability, federation, and maintaining an information feedback loop. It configures near real-time IoT sensor connections, implements event triggers, and delivers 3D visualizations in easily accessible formats. The study tackles critical questions surrounding effective integration of IoT sensor data, identification of crucial flood modeling parameters, and real-time quantification of flood event impacts.
In the first part of the study, a tailored RDT framework for real-time flood modeling is developed, emphasizing IoT sensor connectivity. This framework integrates near real-time data streams to enable advanced analytics, enhancing the accuracy of flood modeling and disaster event monitoring. Key components of the framework include an integrated spatial database, a coupled physical city, and digital space model, and a functional front-end flood interface. The framework aligns with Gemini Principles, focusing on data interoperability and maintaining an information feedback loop. It enables the configuration of near real-time IoT sensor connections, event triggers, and the delivery of 3D visualizations for precise flood modeling. This research drives the application of Digital Twin technology in real-time flood modeling, ultimately enhancing disaster response strategies.
In the second part of the study, an immersive interaction component is introduced to enhance the usability and effectiveness of the RDT framework. Leveraging virtual reality (VR) and game engine technologies, an immersive interface is developed to enable stakeholders to engage with the flood digital twin in a highly intuitive and interactive manner. Users can navigate virtual environments, visualize flood scenarios, and interact with simulation results in real-time. This immersive interaction component enhances stakeholder understanding of flood risks, facilitates collaborative decision-making, and strengthens community resilience. By integrating VR and game engine technologies into the RDT framework, this research expands the capabilities of flood modeling and disaster response strategies, paving the way for more effective and inclusive flood resilience initiatives.
Associate Professor Qian (Chayn) Sun is a researcher specializing in quantitative geography, environmental and urban informatics, and the impacts of urbanization and human movements across spatial and temporal scales. With expertise in developing spatial and statistical algorithms, she focuses on modelling patterns and investigating factors contributing to complex human-environment interactions. Sun's PhD research integrated human perception variables with contextual location information into one conceptual framework. The combination of movement tracking and associated geospatial data collections has significantly improved the understanding of the mechanisms underlying human spatial behaviour and the complex geographical environment. Currently exploring the integration of cloud computing, big Earth observation data, and AI with social-ecological theories, Associate Professor Sun aims to address climate change challenges facing both people and the environment. Through an interdisciplinary approach, she seeks to derive spatially explicit insights to inform strategies for mitigating these challenges. Committed to advancing understanding and finding solutions to real-world problems, Sun strives to make significant contributions to society and the environment.
Paulina PY Wong is currently an Associate Professor and Head of the Science Unit at Lingnan University (Hong Kong) and Associate Director of the LEO Dr David P. Chan Institute of Data Science. She is an environmental geographer specializing in urban climate, air/noise pollution, GIS, and environmental health. In recent years, her research has been extended to GeoAI analytics, urban sensing, mobile geospatial technologies, sustainability education and ESG. She is a certified GIS Professional and ESG Planner (CEP®).