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

Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi John Chang, Sangram Ganguly, Kuo Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, Wen Ping Tsai

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)5639-5656
Number of pages18
JournalHydrology and Earth System Sciences
Volume22
Issue number11
DOIs
StatePublished - Nov 1 2018

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learning
hydrology
science
opinion
coevolution
education
industry
method

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)

Cite this

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. In: Hydrology and Earth System Sciences. 2018 ; Vol. 22, No. 11. pp. 5639-5656.
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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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Shen, C, Laloy, E, Elshorbagy, A, Albert, A, Bales, J, Chang, FJ, Ganguly, S, Hsu, KL, Kifer, D, Fang, Z, Fang, K, Li, D, Li, X & Tsai, WP 2018, 'HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community', Hydrology and Earth System Sciences, vol. 22, no. 11, pp. 5639-5656. https://doi.org/10.5194/hess-22-5639-2018

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

In: Hydrology and Earth System Sciences, Vol. 22, No. 11, 01.11.2018, p. 5639-5656.

Research output: Contribution to journalArticle

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AU - Bales, Jerad

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AU - Fang, Zheng

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AU - Li, Dongfeng

AU - Li, Xiaodong

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