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DOI | 10.1029/2020GL088229 |
Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning | |
Jiang, Shijie1,2; Zheng, Yi1,3; Solomatine, Dimitri4,5,6 | |
2020-06-09 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2020 |
卷号 | 47期号:13 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Singapore; Netherlands; Russia |
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. |
英文关键词 | artificial intelligence deep learning Earth science geosystem dynamics hydrology predictions in ungauged basins |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000551465400036 |
WOS关键词 | BASE-FLOW ; DATA SET ; STREAMFLOW ; ALGORITHM ; PATTERNS ; MODELS |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/274392 |
专题 | 气候变化 |
作者单位 | 1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China; 2.Natl Univ Singapore, Dept Civil & Environm Engn, Singapore, Singapore; 3.Southern Univ Sci & Technol, Shenzhen Municipal Engn Lab Environm IoT Technol, Shenzhen, Peoples R China; 4.IHE Delft Inst Water Educ, Dept Hydroinformat & Sociotech Innovat, Delft, Netherlands; 5.Delft Univ Technol, Dept Water Management, Delft, Netherlands; 6.Russian Acad Sci, Water Problems Inst, Moscow, Russia |
推荐引用方式 GB/T 7714 | Jiang, Shijie,Zheng, Yi,Solomatine, Dimitri. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning[J]. GEOPHYSICAL RESEARCH LETTERS,2020,47(13). |
APA | Jiang, Shijie,Zheng, Yi,&Solomatine, Dimitri.(2020).Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning.GEOPHYSICAL RESEARCH LETTERS,47(13). |
MLA | Jiang, Shijie,et al."Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning".GEOPHYSICAL RESEARCH LETTERS 47.13(2020). |
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