Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Guy Shalev"'
Autor:
Frederik Kratzert, Grey Nearing, Nans Addor, Tyler Erickson, Martin Gauch, Oren Gilon, Lukas Gudmundsson, Avinatan Hassidim, Daniel Klotz, Sella Nevo, Guy Shalev, Yossi Matias
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract 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 st
Externí odkaz:
https://doaj.org/article/5b1436c4feef4818b712c766b37627f2
Akademický článek
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Autor:
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, Massimiliano Zappa
Publikováno v:
Hydrology and Earth System Sciences, 27 (9)
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety ofpredictions from dynamical, physics-based models - such as numerical weather prediction, climate, land, h
Autor:
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, Sella Nevo
Publikováno v:
Hydrology and Earth System Sciences. 26:5493-5513
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
Autor:
Guy Shalev
Publikováno v:
American Anthropologist. 124:688-702
Autor:
Grey Nearing, Martin Gauch, Daniel Klotz, Frederik Kratzert, Asher Metzger, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Oren Gilon
Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow forecast models (e.g., 1-5), the majority of the development and research has been done with hindcast models. The pri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bead22a5df87f8c7001a9e0cc2e569e3
https://doi.org/10.5194/egusphere-egu23-16974
https://doi.org/10.5194/egusphere-egu23-16974
Autor:
Frederik Kratzert, Martin Gauch, Daniel Klotz, Asher Metzger, Grey Nearing, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Oren Gilon
The goal of Google’s Flood Forecasting Initiative is to provide timely and actionable flood warnings to everyone, globally. Until recently, Google provided operational flood warnings only for specific partner countries, namely India, Bangladesh, Sr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92464b7198e9e481fda2646b808254b9
https://doi.org/10.5194/egusphere-egu23-5326
https://doi.org/10.5194/egusphere-egu23-5326
Autor:
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, Grey S. Nearing
Publikováno v:
Hydrology and Earth System Sciences. 26:3377-3392
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
Autor:
Louise Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, Massimiliano Zappa
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fc532ee7007b3a8690f7000f9ac7e65
https://doi.org/10.5194/hess-2022-334
https://doi.org/10.5194/hess-2022-334
A mosaic of sex-related structural changes in the human brain following exposure to real-life stress
Publikováno v:
Brain Structure and Function. 225:461-466
Whereas sex differences in the brain's response to stress have been reported in both humans and animals, it is unknown whether they 'add up' consistently within individual brains. Here, we studied this question in a unique data set of magnetic resona