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

Authors
  • Jiang, Shijie (author)
  • Zheng, Yi (author)
  • Solomatine, D.P. (author)
Publication Date
Jan 01, 2020
Identifiers
DOI: 10.1029/2020GL088229
OAI: oai:tudelft.nl:uuid:8e774b23-95ee-41ba-9bf9-946b3ddb9589
Source
TU Delft Repository
Keywords
Language
English
License
Unknown
External links

Abstract

<p>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.</p> / Water Resources

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