气候变化领域集成服务门户(CCPortal): Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

CCPortal

DOI	10.1029/2020GL088229
	Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning
	Jiang S.; Zheng Y.; Solomatine D.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:13
英文摘要	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. © 2020. The Authors.
英文关键词	Dynamics; Geology; Recurrent neural networks; Earth systems; Environmental studies; Geophysical phenomena; Learning architectures; Model dynamics; Physical approaches; Prediction accuracy; Temporal dynamics; Learning systems; accuracy assessment; artificial intelligence; emergence; instrumentation; machine learning; prediction; runoff; symbiosis; temporal analysis; United States
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170187
作者单位	School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Civil and Environmental Engineering, National University of Singapore, Singapore; Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, Southern University of Science and Technology, Shenzhen, China; Department of Hydroinformatics and Socio-Technical Innovation, IHE Delft Institute for Water Education, Delft, Netherlands; Department of Water Management, Delft University of Technology, Delft, Netherlands; Water Problems Institute, Russian Academy of Sciences, Moscow, Russian Federation
推荐引用方式 GB/T 7714	Jiang S.,Zheng Y.,Solomatine D.. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning[J],2020,47(13).
APA	Jiang S.,Zheng Y.,&Solomatine D..(2020).Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning.Geophysical Research Letters,47(13).
MLA	Jiang S.,et al."Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning".Geophysical Research Letters 47.13(2020).