HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Abstract
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,stimulatemore »
- Authors:
-
[3];
[5];
[9];
- Pennsylvania State Univ., University Park, PA (United States)
- Belgian Nuclear Research Centre, Mol (Belgium)
- Univ. of Saskatchewan, Saskatoon, SK (Canada)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Consortium of Univ. for the Advancement of Hydrologic Science, Inc. (CUAHSI), Cambridge, MA (United States)
- National Taiwan Univ., Taipei (Taiwan)
- NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States)
- Univ. of California, Irvine, CA (United States)
- Univ. of Texas, Arlington, TX (United States)
- Sichuan Univ., Sichuan (China)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Science Foundation (NSF)
- OSTI Identifier:
- 1542338
- Grant/Contract Number:
- AC02-05CH11231; SC0016605; EAR-1832294; EAR-1338606; 2018SZ0343; CCF-1317560
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Hydrology and Earth System Sciences (Online)
- Additional Journal Information:
- Journal Name: Hydrology and Earth System Sciences (Online); Journal Volume: 22; Journal Issue: 11; Journal ID: ISSN 1607-7938
- Publisher:
- European Geosciences Union (EGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Shen, Chaopeng, Laloy, Eric, Elshorbagy, Amin, Albert, Adrian, Bales, Jerad, Chang, Fi-John, Ganguly, Sangram, Hsu, Kuo-Lin, Kifer, Daniel, Fang, Zheng, Fang, Kuai, Li, Dongfeng, Li, Xiaodong, and Tsai, Wen-Ping. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. United States: N. p., 2018.
Web. doi:10.5194/hess-22-5639-2018.
Shen, Chaopeng, Laloy, Eric, Elshorbagy, Amin, Albert, Adrian, Bales, Jerad, Chang, Fi-John, Ganguly, Sangram, Hsu, Kuo-Lin, Kifer, Daniel, Fang, Zheng, Fang, Kuai, Li, Dongfeng, Li, Xiaodong, & Tsai, Wen-Ping. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. United States. doi:10.5194/hess-22-5639-2018.
Shen, Chaopeng, Laloy, Eric, Elshorbagy, Amin, Albert, Adrian, Bales, Jerad, Chang, Fi-John, Ganguly, Sangram, Hsu, Kuo-Lin, Kifer, Daniel, Fang, Zheng, Fang, Kuai, Li, Dongfeng, Li, Xiaodong, and Tsai, Wen-Ping. Thu .
"HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community". United States. doi:10.5194/hess-22-5639-2018. https://www.osti.gov/servlets/purl/1542338.
@article{osti_1542338,
title = {HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community},
author = {Shen, Chaopeng and Laloy, Eric and Elshorbagy, Amin and Albert, Adrian and Bales, Jerad and Chang, Fi-John and Ganguly, Sangram and Hsu, Kuo-Lin and Kifer, Daniel and Fang, Zheng and Fang, Kuai and Li, Dongfeng and Li, Xiaodong and Tsai, Wen-Ping},
abstractNote = {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.},
doi = {10.5194/hess-22-5639-2018},
journal = {Hydrology and Earth System Sciences (Online)},
number = 11,
volume = 22,
place = {United States},
year = {2018},
month = {11}
}
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