Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Grey S. Nearing"'
Autor:
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, Sella Nevo
Publikováno v:
Hydrology and Earth System Sciences. 26:5493-5513
Ingesting near-real-time observation data is a critical component of many operational hydrological forecasting systems. In this paper, we compare two strategies for ingesting near-real-time streamflow observations into long short-term memory (LSTM) r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b373ba75c35030ff10e1f7ce1d33c2f7
https://doi.org/10.5194/hess-26-5493-2022
https://doi.org/10.5194/hess-26-5493-2022
Autor:
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, Grey S. Nearing
Publikováno v:
Hydrology and Earth System Sciences. 26:3377-3392
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that the predictive accuracy of data-driven models based on deep learning may not be reliable in extrapolation or for predicti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13eeb01d12d32d95d47dca41f69c12bb
https://doi.org/10.5194/hess-26-3377-2022
https://doi.org/10.5194/hess-26-3377-2022
Publikováno v:
Hydrological Processes. 37
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::628bb92d79c1d85a5008faf0fe4d5bb0
https://doi.org/10.1002/hyp.14847
https://doi.org/10.1002/hyp.14847
Publikováno v:
Journal of Hydrology. 614:128551
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::accb93430e218a9d33f90e73e297a025
https://doi.org/10.1016/j.jhydrol.2022.128551
https://doi.org/10.1016/j.jhydrol.2022.128551
A deep learning rainfall-runoff model can take multiple meteorological forcing products as inputs and learn to combine them in spatially and temporally dynamic ways. This is demonstrated using Long Short Term Memory networks (LSTMs) trained over basi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfd04bb6dfe16b19b2901441a05d5db5
https://doi.org/10.31223/osf.io/pjm5a
https://doi.org/10.31223/osf.io/pjm5a
Publikováno v:
Handbook of Hydrometeorological Ensemble Forecasting ISBN: 9783642404573
Handbook of Hydrometeorological Ensemble Forecasting
Handbook of Hydrometeorological Ensemble Forecasting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55c52be171016859b13def1a0d3d1b54
https://doi.org/10.1007/978-3-642-39925-1_30
https://doi.org/10.1007/978-3-642-39925-1_30