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

@inproceedings{Shen2018HESSOI,
  title={HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community},
  author={Chaopeng Shen and Eric Laloy and Amin Elshorbagy and Adrian Albert and Jerad D. Bales and F. J. Chang and Sangram Ganguly and Kuolin Hsu and Daniel Kifer and Zheng Fang and Kuai Fang and Dongfeng Li and Xiaodong Li and Wen Ping Tsai},
  year={2018}
}
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… CONTINUE READING
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