Zobrazeno 1 - 10
of 825
pro vyhledávání: '"A. Kratzert"'
Autor:
Shalev, Guy, Kratzert, Frederik
The Caravan large-sample hydrology dataset (Kratzert et al., 2023) was created to standardize and harmonize streamflow data from various regional datasets, combined with globally available meteorological forcing and catchment attributes. This communi
Externí odkaz:
http://arxiv.org/abs/2411.09459
Autor:
Nearing, Grey, Cohen, Deborah, Dube, Vusumuzi, Gauch, Martin, Gilon, Oren, Harrigan, Shaun, Hassidim, Avinatan, Klotz, Daniel, Kratzert, Frederik, Metzger, Asher, Nevo, Sella, Pappenberger, Florian, Prudhomme, Christel, Shalev, Guy, Shenzis, Shlomo, Tekalign, Tadele, Weitzner, Dana, Matias, Yoss
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula
Externí odkaz:
http://arxiv.org/abs/2307.16104
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:
Nevo, Sella, Morin, Efrat, Rosenthal, Adi Gerzi, Metzger, Asher, Barshai, Chen, Weitzner, Dana, Voloshin, Dafi, Kratzert, Frederik, Elidan, Gal, Dror, Gideon, Begelman, Gregory, Nearing, Grey, Shalev, Guy, Noga, Hila, Shavitt, Ira, Yuklea, Liora, Royz, Moriah, Giladi, Niv, Levi, Nofar Peled, Reich, Ofir, Gilon, Oren, Maor, Ronnie, Timnat, Shahar, Shechter, Tal, Anisimov, Vladimir, Gigi, Yotam, Levin, Yuval, Moshe, Zach, Ben-Haim, Zvika, Hassidim, Avinatan, Matias, Yossi
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded ge
Externí odkaz:
http://arxiv.org/abs/2111.02780
Autor:
Hoedt, Pieter-Jan, Kratzert, Frederik, Klotz, Daniel, Halmich, Christina, Holzleitner, Markus, Nearing, Grey, Hochreiter, Sepp, Klambauer, Günter
The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related tasks with random weights, meaning without learning. Similarly, Long
Externí odkaz:
http://arxiv.org/abs/2101.05186
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 6, Pp n/a-n/a (2024)
Abstract Sap flow offers key insights about transpiration dynamics and forest‐climate interactions. Accurately simulating sap flow remains challenging due to measurement uncertainties and interactions between global and local environmental controls
Externí odkaz:
https://doaj.org/article/bd7648fe6bab4c8f89ca4eeb9b886cdf
Publikováno v:
Journal for ImmunoTherapy of Cancer, Vol 12, Iss Suppl 1 (2024)
Externí odkaz:
https://doaj.org/article/cf52905d01334b69b43b01f72fdda3c6
Autor:
Klotz, Daniel, Kratzert, Frederik, Gauch, Martin, Sampson, Alden Keefe, Klambauer, Günter, Hochreiter, Sepp, Nearing, Grey
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 forecasting, and while standardi
Externí odkaz:
http://arxiv.org/abs/2012.14295