Publication Date:
2018
Description:
〈b〉HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community〈/b〉〈br〉
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, and Wen-Ping Tsai〈br〉
Hydrol. Earth Syst. Sci., 22, 5639-5656, https://doi.org/10.5194/hess-22-5639-2018, 2018〈br〉
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.
Print ISSN:
1027-5606
Electronic ISSN:
1607-7938
Topics:
Geography
,
Geosciences
