Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Marvin Höge"'
Autor:
Thiago V. M. do Nascimento, Julia Rudlang, Marvin Höge, Ruud van der Ent, Máté Chappon, Jan Seibert, Markus Hrachowitz, Fabrizio Fenicia
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-19 (2024)
Abstract Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information fro
Externí odkaz:
https://doaj.org/article/589176f22a01472ea2058c7cc70f412f
Publikováno v:
Frontiers in Artificial Intelligence, Vol 3 (2020)
Methods for sequential design of computer experiments typically consist of two phases. In the first phase, the exploratory phase, a space-filling initial design is used to estimate hyperparameters of a Gaussian process emulator (GPE) and to provide s
Externí odkaz:
https://doaj.org/article/e3c59200ea1542c4bf90bef2e742f10b
Publikováno v:
Water, Vol 12, Iss 2, p 309 (2020)
Model averaging makes it possible to use multiple models for one modelling task, like predicting a certain quantity of interest. Several Bayesian approaches exist that all yield a weighted average of predictive distributions. However, often, they are
Externí odkaz:
https://doaj.org/article/3e42c92f1c3b4c1e8744f6773cfd0511
Autor:
Aline Schäfer Rodrigues Silva, Tobias K. D. Weber, Sebastian Gayler, Anneli Guthke, Marvin Höge, Wolfgang Nowak, Thilo Streck
Publikováno v:
Modeling Earth Systems and Environment. 8:5143-5175
There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Prev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16341ce221c3c3109af956e6c0427c1c
https://doi.org/10.1007/s40808-022-01427-1
https://doi.org/10.1007/s40808-022-01427-1
Neural Ordinary Differential Equation (ODE) models have demonstrated high potential in providing accurate hydrologic predictions and process understanding for single catchments (Höge et al., 2022). Neural ODEs fuse a neural network model core with a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6975134c986f879f1756713ac404631
https://doi.org/10.5194/egusphere-egu23-6466
https://doi.org/10.5194/egusphere-egu23-6466
Autor:
Rosi Siber, Marvin Höge, Martina Kauzlaric, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Daniel Viviroli, Massimiliano Zappa, Anna E. Sikorska-Senoner, Sandra Pool, Marius Günter Floriancic, Peter Reichert, Jan Seibert, Nans Addor, Bettina Schaefli, Fabrizio Fenicia
Publikováno v:
Siber, Rosi; Höge, Marvin; Kauzlaric, Martina; Schönenberger, Ursula; Horton, Pascal; Schwanbeck, Jan; Viviroli, Daniel; Zappa, Massimiliano; Sikorska-Senoner, Anna E.; Pool, Sandra; Floriancic, Marius Günter; Reichert, Peter; Seibert, Jan; Addor, Nans; Schaefli, Bettina; Fenicia, Fabrizio (May 2022). CAMELS-CH-Building a Common Open Database for Catchments in Switzerland. In: EGU General Assembly 2022. Vienna, Austria. 23–27 May 2022. 10.5194/egusphere-egu22-9859
Over recent years, numerous open catchment datasets have been published. In 2017, the first CAMELS (catchment attributes and meteorology for large-sample studies) dataset was released for the continental US by Addor et al. (2017). It comprises data f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5285836eec4430cb98f2b7ddb4e02a2
https://doi.org/10.5194/egusphere-egu22-9859
https://doi.org/10.5194/egusphere-egu22-9859
Deep learning methods have repeatedly proven to outperform conceptual hydrologic models in rainfall-runoff modelling. Although attempts of investigating the internals of such deep learning models are being made, traceability of model states and proce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::489e62bcb8d55021258ab737f6aa488c
https://doi.org/10.5194/egusphere-egu22-3661
https://doi.org/10.5194/egusphere-egu22-3661