Primary Convener:
Jiangtao Liu, Pennsylvania State University

Convener:
Chaopeng Shen, Pennsylvania State University Main Campus
Dan Lu, Oak Ridge National Laboratory
Yalan Song, Pennsylvania State University Main Campus

Early Career Convener:
Yuan Yang, University of California San Diego

Chair:
Jiangtao Liu, Pennsylvania State University
Yalan Song, Pennsylvania State University Main Campus
Yuan Yang, University of California San Diego
Dan Lu, Oak Ridge National Laboratory

Frontier artificial intelligence (AI) models, including transformers, diffusion models, and long short-term memory (LSTM), are advancing our ability to model, interpret, and predict complex hydrological processes. This session highlights cutting-edge applications of AI in water science, with a focus on hydrologic modeling, uncertainty quantification, model interpretability, and the integration of physical knowledge into data-driven frameworks. We welcome submissions on topics including, but not limited to: (1) Applications of AI models, such as transformers, LSTM, diffusion models, and large-scale pretrained foundation models for improving model accuracy and transferability; (2) Interpretable AI approaches for enhancing transparency in water modeling; (3) Uncertainty quantification in water science applications; (4) Use of large language models (LLMs) for information extraction or decision support in water resource management; (5) Integration of AI with multi-source data, such as remote sensing; and (6) Hybrid models that integrate physics‑based hydrological approaches with deep learning techniques.

Index Terms
1805 Computational hydrology
1839 Hydrologic scaling
1847 Modeling
1873 Uncertainty assessment

Suggested Itineraries:
Machine Learning and AI
Open Science and Open Data

Neighborhoods:
3. Earth Covering