Zobrazeno 1 - 10
of 825
pro vyhledávání: '"A Kratzert"'
Publikováno v:
Journal for ImmunoTherapy of Cancer, Vol 12, Iss Suppl 1 (2024)
Externí odkaz:
https://doaj.org/article/cf52905d01334b69b43b01f72fdda3c6
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
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
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
Autor:
Gauch, Martin, Kratzert, Frederik, Klotz, Daniel, Nearing, Grey, Lin, Jimmy, Hochreiter, Sepp
Publikováno v:
Hydrol. Earth Syst. Sci., 25, 2045-2062, 2021
Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate prediction of short but extrem
Externí odkaz:
http://arxiv.org/abs/2010.07921
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the climate changes, precipitation and rainfall-runoff pattern variations be
Externí odkaz:
http://arxiv.org/abs/2007.00595
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. Recent developments in rainfall-runoff modeling, one of the prime chall
Externí odkaz:
http://arxiv.org/abs/1911.09427