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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 11 | Copyright

Special issue: HESS Opinions

Hydrol. Earth Syst. Sci., 22, 5639-5656, 2018
https://doi.org/10.5194/hess-22-5639-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Opinion article 01 Nov 2018

Opinion article | 01 Nov 2018

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Chaopeng Shen1, Eric Laloy2, Amin Elshorbagy3, Adrian Albert4, Jerad Bales5, Fi-John Chang6, Sangram Ganguly7, Kuo-Lin Hsu8, Daniel Kifer9, Zheng Fang10, Kuai Fang1, Dongfeng Li10, Xiaodong Li11, and Wen-Ping Tsai1 Chaopeng Shen et al.
  • 1Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, USA
  • 2Institute for Environment, Health and Safety, Belgian Nuclear Research Centre, Mol, Belgium
  • 3Dept. of Civil, Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, Canada
  • 4National Energy Research Supercomputing Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • 5Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), Cambridge, MA, USA
  • 6Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan
  • 7NASA Ames Research Center/BAER Institute, Moffett Field, CA 94035, USA
  • 8Civil and Environmental Engineering, University of California, Irvine, Irvine, CA 92697, USA
  • 9Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802, USA
  • 10Civil Engineering, University of Texas at Arlington, Arlington, TX 76013, USA
  • 11State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Sichuan, China

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, stimulate advances in machine learning as well.

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Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and...
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