Please join CEE, PEI and CSML for a “Special” virtual Seminar.

Wednesday, October 14^th at 8:00 pm Eastern Time (USA and Canada)

Abstract:

Nowadays, all sorts of sensors, from ground to space, collect a huge volume of data about the Earth. Recent advances in artificial intelligence (AI) provide unprecedented opportunities for data-driven hydrological modeling using such “Big Earth Data”. However, many critical issues remain to be addressed. For example, there lacks efficient frameworks for data-driven modeling under the big data condition. Moreover, with the amazing predicting power of deep learning (DL) demonstrated in the field of hydrology, whether and how hydrology theory can still play an important role in hydrological modeling is under debate. This presentation introduces two recent studies conducted by Prof. Yi Zheng’s group which attempt to address the above issues. A novel approach to data-driven hydrological modeling was first developed. The approach adopts computer vision techniques to effectively exploit spatial information contained in dynamic input data fields and seamlessly fuse multisource data with huge data volumes via machine learning. The second study develops a novel DL framework which contains a special recurrent neural layer to “memorize” physical rules behind system dynamics. Following this framework, a conceptual hydrologic model was encoded into a DL structure, leading to a hydrology-aware DL model. The model was implemented to simulate runoff in 569 catchments across the conterminous United States. The simulation results show that this hybrid AI model has enhanced prediction accuracy, robust transferability, and good intelligence for inferring unobserved processes. Overall, this presentation stresses that physics-AI integration is a promising direction for hydrological modeling in the era of big data.

Prof. Dr. Yi Zheng received his Ph.D. from University of California, Santa Barbara (2007). He is currently the Associate Dean of the School of Environmental Science and Engineering at Southern University of Science and Technology (SUSTech), China. Before he joined SUSTech in 2016, he was an associate professor at Peking University. His current research interests include hydrologic modeling, water resources management and environmental big data. His major scientific contributions cover integrated ecohydrological modeling, uncertainty analysis for complex environmental models, human-water nexus, and artificial intelligence for hydrology. He has published over 90 peer-reviewed papers (including 7 ESI highly cited paper), mostly in top-tier journals of Earth and environmental sciences, such as Geophysical Research Letters, Water Resources Research, Remote Sensing of Environment, Water Research, Environmental Modelling & Software, etc. He currently serves as an associated editor for Water Resources Research and Journal of Hydrologic Engineering-ASCE. He received Excellent Young Investigator Award from National Natural Science Foundation of China (2016) and Outstanding Research Award from China Society of Natural Resources (2019).

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https://princeton.zoom.us/j/91979274321