Calibrating hydrological models with consistent scoring functions

Autor: Tyralis, H., Papacharalampous, G., Khatami, S.

Publikováno v: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)

Predictive uncertainty in hydrological modelling is quantified by using post-processing methods or Bayesian statistical models. The former methods are not straightforward because they combine two models of different nature while the latter methods ar

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Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS

Autor: Tyralis, H. Papacharalampous, G. Burnetas, A. Langousis, A.

Post-processing of hydrological model simulations using machine learning algorithms can be applied to quantify the uncertainty of hydrological predictions. Combining multiple diverse machine learning algorithms (referred to as base-learners) using st

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https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3005443

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Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity.

Autor: Papacharalampous G; Department of Engineering, Roma Tre University, Rome, Italy; Department of Civil Engineering, School of Engineering, University of Patras, University Campus, Rio, 26504 Patras, Greece; Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, 15780 Zographou, Greece. Electronic address: papacharalampous.georgia@gmail.com., Tyralis H; Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, 15780 Zographou, Greece; Air Force Support Command, Hellenic Air Force, Elefsina Air Base, 19200 Elefsina, Greece. Electronic address: montchrister@gmail.com., Papalexiou SM; Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; Global Institute for Water Security, Saskatoon, Saskatchewan, Canada; Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Czech Republic. Electronic address: sm.papalexiou@usask.ca., Langousis A; Department of Civil Engineering, School of Engineering, University of Patras, University Campus, Rio, 26504 Patras, Greece. Electronic address: andlag@alum.mit.edu., Khatami S; Department of Physical Geography and the Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden; Climate & Energy College, University of Melbourne, Parkville, Victoria, Australia; Department of Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia. Electronic address: sina.khatami@natgeo.su.se., Volpi E; Department of Engineering, Roma Tre University, Rome, Italy. Electronic address: elena.volpi@uniroma3.it., Grimaldi S; Department for Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, Viterbo, Italy; Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 10003, USA. Electronic address: salvatore.grimaldi@unitus.it.

Publikováno v: The Science of the total environment [Sci Total Environ] 2021 May 01; Vol. 767, pp. 144612. Date of Electronic Publication: 2020 Dec 25.