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

Shijie Jiang, Yi Zheng, Dimitri Solomatine

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
17 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article numbere2020GL088229
Number of pages11
JournalGeophysical Research Letters
Volume47
Issue number13
DOIs
Publication statusPublished - 2020

Keywords

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

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