globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-5639-2018
论文题名:
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
作者: Shen C.; Laloy E.; Elshorbagy A.; Albert A.; Bales J.; Chang F.-J.; Ganguly S.; Hsu K.-L.; Kifer D.; Fang Z.; Fang K.; Li D.; Li X.; Tsai W.-P.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:11
起始页码: 5639
结束页码: 5656
语种: 英语
Scopus关键词: Artificial intelligence ; Hydrology ; Learning algorithms ; Data limitations ; Hydrologic science ; Industry applications ; Integrating process ; Research opportunities ; Science education ; Scientific advances ; Scientific discovery ; Deep learning ; algorithm ; applied science ; education ; heterogeneity ; hydrology ; knowledge ; machine learning ; technology adoption
英文摘要: Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well. © 2018 Author(s).
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163151
Appears in Collections:气候变化与战略

Files in This Item:

There are no files associated with this item.


作者单位: Shen, C., Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, United States; Laloy, E., Institute for Environment, Health and Safety, Belgian Nuclear Research Centre, Mol, Belgium; Elshorbagy, A., Dept. of Civil, Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, Canada; Albert, A., National Energy Research Supercomputing Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States; Bales, J., Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI), Cambridge, MA, United States; Chang, F.-J., Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan; Ganguly, S., NASA Ames Research Center, BAER Institute, Moffett Field, CA 94035, United States; Hsu, K.-L., Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, United States; Kifer, D., Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802, United States; Fang, Z., Department of Civil Engineering, University of Texas, Arlington, TX 76013, United States; Fang, K., Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, United States; Li, D., Department of Civil Engineering, University of Texas, Arlington, TX 76013, United States; Li, X., State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Sichuan, China; Tsai, W.-P., Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, United States

Recommended Citation:
Shen C.,Laloy E.,Elshorbagy A.,et al. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community[J]. Hydrology and Earth System Sciences,2018-01-01,22(11)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Shen C.]'s Articles
[Laloy E.]'s Articles
[Elshorbagy A.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Shen C.]'s Articles
[Laloy E.]'s Articles
[Elshorbagy A.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Shen C.]‘s Articles
[Laloy E.]‘s Articles
[Elshorbagy A.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.