Towards physics-AI hybrid modeling in hydrology: Opportunities and challenges
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Recent advances in AI provide unprecedented opportunities to predict and understand components of the hydrological cycles. Nonetheless, many critical issues remain unresolved. For instance, with the remarkable predicting power of machine learning demonstrated in the field, it is under debate whether and how hydrological theory can still have a place in hydrological models or even benefit from “black-box” machine learning methods. In this presentation, we introduce two case studies that attempt to incorporate hydrological knowledge and machine learning in a unified framework. The first study develops a novel deep learning architecture that is encoded with a conceptual hydrological model, resulting in an end-to-end hydrology-AI hybrid learning system. The simulation results show that the hybrid model has improved prediction accuracy, robust transferability, and good intelligence for inferring unobserved processes. The second study develops an explainable machine learning approach to improve our understanding of runoff mechanisms, thereby gaining a deeper insight into how floods are changing under climate change. Overall, this presentation stresses how physics-AI integration can help us understand aspects of the Earth system and the potential opportunities and challenges associated with it.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.