Zobrazeno 1 - 10
of 609
pro vyhledávání: '"D Klotz"'
Publikováno v:
Hydrology and Earth System Sciences, Vol 28, Pp 4099-4126 (2024)
Uncertainty estimates are fundamental to assess the reliability of predictive models in hydrology. We use the framework of conformal prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological
Externí odkaz:
https://doaj.org/article/d52b2f4ae3a246aaa6ab9bfc7da9822b
Publikováno v:
Hydrology and Earth System Sciences, Vol 28, Pp 4187-4201 (2024)
Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow
Externí odkaz:
https://doaj.org/article/1390265d747745d3be4d827ff8e2ee92
Publikováno v:
Hydrology and Earth System Sciences, Vol 28, Pp 3665-3673 (2024)
The evaluation of model performance is an essential part of hydrological modeling. However, leveraging the full information that performance criteria provide requires a deep understanding of their properties. This Technical Note focuses on a rather c
Externí odkaz:
https://doaj.org/article/77337badc80247d6a9404d06010c1178
Autor:
G. S. Nearing, D. Klotz, J. M. Frame, M. Gauch, O. Gilon, F. Kratzert, A. K. Sampson, G. Shalev, S. Nevo
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 5493-5513 (2022)
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://doaj.org/article/b85f88847fe5433c93431e7f7cad3a51
Autor:
J. M. Frame, F. Kratzert, D. Klotz, M. Gauch, G. Shelev, O. Gilon, L. M. Qualls, H. V. Gupta, G. S. Nearing
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 3377-3392 (2022)
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://doaj.org/article/1ef8b1f9cdd0413c9f0aea6a8ad269f1
Autor:
J. Mai, H. Shen, B. A. Tolson, É. Gaborit, R. Arsenault, J. R. Craig, V. Fortin, L. M. Fry, M. Gauch, D. Klotz, F. Kratzert, N. O'Brien, D. G. Princz, S. Rasiya Koya, T. Roy, F. Seglenieks, N. K. Shrestha, A. G. T. Temgoua, V. Vionnet, J. W. Waddell
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 3537-3572 (2022)
Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region
Externí odkaz:
https://doaj.org/article/416ccf5f896c42c79cb9882e6360483a
Autor:
T. Lees, S. Reece, F. Kratzert, D. Klotz, M. Gauch, J. De Bruijn, R. Kumar Sahu, P. Greve, L. Slater, S. J. Dadson
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 3079-3101 (2022)
Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information
Externí odkaz:
https://doaj.org/article/15d321d05fcc4777b91ade7655980619
Autor:
D. Klotz, F. Kratzert, M. Gauch, A. Keefe Sampson, J. Brandstetter, G. Klambauer, S. Hochreiter, G. Nearing
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 1673-1693 (2022)
Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable hydrological prediction, and while standardiz
Externí odkaz:
https://doaj.org/article/96cb5376c817430ea1c485377f096f4c
Publikováno v:
Hydrology and Earth System Sciences, Vol 25, Pp 2685-2703 (2021)
A deep learning rainfall–runoff model can take multiple meteorological forcing products as input and learn to combine them in spatially and temporally dynamic ways. This is demonstrated with Long Short-Term Memory networks (LSTMs) trained over basi
Externí odkaz:
https://doaj.org/article/15eccdbbc4a446e48fa4b89afa592857
Publikováno v:
Hydrology and Earth System Sciences, Vol 25, Pp 2045-2062 (2021)
Long Short-Term Memory (LSTM) networks have been applied to daily discharge prediction with remarkable success. Many practical applications, however, require predictions at more granular timescales. For instance, accurate prediction of short but extr
Externí odkaz:
https://doaj.org/article/43e618bd45a540ac9d523f9533799dba