Improving AI System Awareness of Geoscience Knowledge

Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

Jiang, Shijie (Southern University of Science and Technology; National University of Singapore)
Zheng, Yi (Southern University of Science and Technology)
Solomatine, D.P. (TU Delft Water Resources; IHE Delft Institute for Water Education; Russian Academy of Sciences)

2020

Modeling dynamic geophysical phenomena is at the core of Earth and environmental studies. The geoscientific community relying mainly on physical representations may want to consider much deeper adoption of artificial intelligence (AI) instruments in the context of AI's global success and emergence of big Earth data. A new perspective of using hybrid physics-AI approaches is a grand vision, but actualizing such approaches remains an open question in geoscience. This study develops a general approach to improving AI geoscientific awareness, wherein physical approaches such as temporal dynamic geoscientific models are included as special recurrent neural layers in a deep learning architecture. The illustrative case of runoff modeling across the conterminous United States demonstrates that the physics-aware DL model has enhanced prediction accuracy, robust transferability, and good intelligence for inferring unobserved processes. This study represents a firm step toward realizing the vision of tackling Earth system challenges by physics-AI integration.

artificial intelligence
deep learning
Earth science
geosystem dynamics
hydrology
predictions in ungauged basins

http://resolver.tudelft.nl/uuid:8e774b23-95ee-41ba-9bf9-946b3ddb9589

https://doi.org/10.1029/2020GL088229

0094-8276

Geophysical Research Letters, 47 (13)

Institutional Repository

journal article

© 2020 Shijie Jiang, Yi Zheng, D.P. Solomatine