H059
Early Career Researchers Advancing Hydrology with AI and Big Data
Early Career Researchers Advancing Hydrology with AI and Big Data
Session ID#: 248932
Session Description:
Early-career scientists are revolutionizing hydrological sciences by pioneering innovative applications of big data and machine learning (ML). This session highlights their transformative contributions in advancing the field while fostering collaborations, showcasing cutting-edge research, and discussing the challenges and opportunities of integrating ML and big data in hydrology from the perspectives of emerging scientists. We welcome contributions from early-career researchers at all stages, focusing on: (1) Development and harmonization of large-scale datasets; (2) Large-scale hydrologic modeling; (3) Integration of ML with process-based models and remote sensing; (4) Data-driven insights on fundamental water science and management.
Index Terms:
1829 Groundwater hydrology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1860 Streamflow [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
Primary Convener: Yalan Song, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States
Conveners: Frederick Y Cheng, University of Virginia, Department of Environmental Sciences, Charlottesville, United States, Wei Zhi, Hohai University, Nanjing, China and Zach Perzan, University of Nevada, Las Vegas, Geoscience, Las Vegas, United States
Student/Early Career Convener: Shuyu Y Chang, The Pennsylvania State University, Department of Geography, University Park, United States
See more of: Hydrology