Results 11 to 20 of about 412,263 (413)
NeuralHydrology -- Interpreting LSTMs in Hydrology [PDF]
In: Samek W., Montavon G., Vedaldi A., Hansen L., Muller KR. (eds)
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.
Lecture Notes in Computer Science, vol 11700. Springer, Cham, 2019, 2019 Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which ...
Frederik Kratzert +4 more
arxiv +2 more sources
50 Years with Nordic Hydrology/Hydrology Research
Hydrology Research, 2022 Abstract The history of Nordic Hydrology/Hydrology Research is described from its initiation in 1970 to its current state in 2021. This includes dramatic changes leading first to an ownership transformation to Nordic Association for Hydrology (NAH) in 1976, and much later, in 2004, to a joint ownership between NAH, British Hydrological ...
Dan Rosbjerg
openaire +4 more sources
The future of Earth observation in hydrology [PDF]
Hydrology and Earth System Sciences, 2017 In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone
A. Lucieer +13 more
core +7 more sources
Caravan - A global community dataset for large-sample hydrology
Scientific Data, 2023 High-quality datasets are essential to support hydrological science and modeling. Several CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist for specific countries or regions, however these datasets lack standardization,
Frederik Kratzert +11 more
semanticscholar +1 more source
Acknowledgment to the Reviewers of Hydrology in 2022
Hydrology, 2023 High-quality academic publishing is built on rigorous peer review [...]
Hydrology Editorial Office
doaj +1 more source
A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources [PDF]
Water Science and Technology, 2020 The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water ...
M. Sit +5 more
semanticscholar +1 more source
Hydrological post-processing for predicting extreme quantiles [PDF]
Journal of Hydrology 617(Part C) (2023) 129082, 2022 Hydrological post-processing using quantile regression algorithms constitutes a prime means of estimating the uncertainty of hydrological predictions. Nonetheless, conventional large-sample theory for quantile regression does not apply sufficiently far in the tails of the probability distribution of the dependent variable.
arxiv +1 more source
Acknowledgment to Reviewers of Hydrology in 2020
Hydrology, 2021 Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Hydrology maintains its standards for the high quality of its published papers [...]
Hydrology Editorial Office
doaj +1 more source
Quantile-based hydrological modelling [PDF]
Water 13 (2021) 3420, 2021 Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e. assumptions on the probability distribution of the hydrological model's output are necessary).
arxiv +1 more source
Acknowledgment to Reviewers of Hydrology in 2021
Hydrology, 2022 Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Hydrology Editorial Office
doaj +1 more source