气候变化领域集成服务门户(CCPortal): HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

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DOI	10.5194/hess-22-5639-2018
	HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
	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.
发表日期	2018
ISSN	1027-5606
起始页码	5639
结束页码	5656
卷号	22 期号:11
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).
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159868
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, Pen...
推荐引用方式 GB/T 7714	Shen C.,Laloy E.,Elshorbagy A.,et al. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community[J],2018,22(11).
APA	Shen C..,Laloy E..,Elshorbagy A..,Albert A..,Bales J..,...&Tsai W.-P..(2018).HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community.Hydrology and Earth System Sciences,22(11).
MLA	Shen C.,et al."HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community".Hydrology and Earth System Sciences 22.11(2018).