Search Results - G. Nearing

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  1. 1

    HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin by F. Kratzert, M. Gauch, D. Klotz, G. Nearing

    Published in Hydrology and Earth System Sciences (2024-09-01)
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  2. 2

    Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets by F. Kratzert, D. Klotz, G. Shalev, G. Klambauer, S. Hochreiter, G. Nearing

    Published in Hydrology and Earth System Sciences (2019-12-01)
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  3. 3

    A data-centric perspective on the information needed for hydrological uncertainty predictions by A. Auer, M. Gauch, F. Kratzert, G. Nearing, S. Hochreiter, D. Klotz

    Published in Hydrology and Earth System Sciences (2024-09-01)
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  4. 4

    Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions by D. Klotz, M. Gauch, F. Kratzert, G. Nearing, J. Zscheischler, J. Zscheischler

    Published in Hydrology and Earth System Sciences (2024-08-01)
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  5. 5

    Uncertainty estimation with deep learning for rainfall–runoff modeling by D. Klotz, F. Kratzert, M. Gauch, A. Keefe Sampson, J. Brandstetter, G. Klambauer, S. Hochreiter, G. Nearing

    Published in Hydrology and Earth System Sciences (2022-03-01)
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  6. 6

    On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results by G. Abramowitz, G. Abramowitz, A. Ukkola, A. Ukkola, S. Hobeichi, S. Hobeichi, J. Cranko Page, J. Cranko Page, M. Lipson, M. G. De Kauwe, S. Green, S. Green, C. Brenner, C. Brenner, J. Frame, G. Nearing, M. Clark, M. Best, P. Anthoni, G. Arduini, S. Boussetta, S. Caldararu, S. Caldararu, K. Cho, M. Cuntz, D. Fairbairn, C. R. Ferguson, H. Kim, Y. Kim, J. Knauer, J. Knauer, D. Lawrence, X. Luo, S. Malyshev, T. Nitta, J. Ogee, K. Oleson, C. Ottlé, P. Peylin, P. de Rosnay, H. Rumbold, B. Su, N. Vuichard, A. P. Walker, X. Wang-Faivre, Y. Wang, Y. Zeng

    Published in Biogeosciences (2024-12-01)
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