We’re very happy to welcome Fangxin Fang, from Imperial’s Department of Earth Science & Engineering to give a seminar on Tuesday, 30th January, on their work focusing on predictive modelling for environmental problems.
Hybrid AI and physical modelling for accurate and rapid environmental prediction & management
Developing a hybrid model that integrates physics-based principles with advanced artificial intelligence (AI) techniques is a promising strategy for achieving accurate and efficient environmental prediction. This hybrid approach harnesses the strengths of both disciplines to enhance the precision and versatility of predictive modelling in the realm of environmental sciences. In this framework, the physics-based component incorporates established principles from fluid dynamics, thermodynamics, and other relevant physical disciplines. These principles form the foundational understanding of how environmental systems operate, providing a solid basis for modelling complex interactions at various scales (global, regional, city, building, street scales down to personal scales).
In this talk, I will first demonstrate the capability of multis-scale adaptive mesh physical modelling for urban environmental problems, where, the details of buildings, and impact of green infrastructures (trees, parks) are considered. Furthermore, I will introduce recently development on machine learning techniques and data assimilation for improved predictive accuracy and uncertainty optimisation and rapid responding modelling. Complementing the physics-based foundation, AI algorithms are employed to dynamically adapt and refine the model based on real-time data. Machine learning techniques, such as neural networks and data assimilation, enable the model to learn from observed environmental patterns, account for uncertainties, and continuously improve its predictive accuracy. The capability of deep learning combined with data assimilation is demonstrated through hourly/daily PM2.5/ozone forecasting globally and regionally (in China), which is a challenging task due to the complexity of geological and meteorological conditions in the region, the need for high-resolution forecasting over a large study area, and the scarcity of observations. Finally, the presentation underscores the significance of digital twin tools in the context of smart city management, drawing connections to a recently funded EPSRC project. This holistic approach not only showcases the potential of a hybrid physics-AI model in environmental prediction but also emphasizes its practical implications for advancing smart city initiatives.