Selecting optimal hydrodynamic model complexity using a Bayesian description of bias

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F13%3A00225888" target="_blank" >RIV/68407700:21110/13:00225888 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Selecting optimal hydrodynamic model complexity using a Bayesian description of bias

Popis výsledku v původním jazyce

Rainfall-runoff models only simplistically reproduce the hydrological and hydraulic processes in real-world hydrosystems, usually generating biased predictions. Among others, the main sources of this bias are model deficits, which are usually very challenging to quantify and thus have widely escaped a rigorous assessment. Model deficits can be influenced to some degree by choosing either a simpler model, usually with less processes and/or parameters, or a more complex one. Selecting the best parametrization of the system behavior is of high relevance for engineers and scientists dealing with flow predictions. Indeed, it is so far not clear how to choose the optimal structural complexity for less biased and computationally-feasible predictions. The goals of this study are i) to statistically assess the reduction of structural uncertainty when increasing model complexity and ii) to select optimal model configuration considering the width of the predictive uncertainty intervals, magnitude

Název v anglickém jazyce

Selecting optimal hydrodynamic model complexity using a Bayesian description of bias

Popis výsledku anglicky

Rainfall-runoff models only simplistically reproduce the hydrological and hydraulic processes in real-world hydrosystems, usually generating biased predictions. Among others, the main sources of this bias are model deficits, which are usually very challenging to quantify and thus have widely escaped a rigorous assessment. Model deficits can be influenced to some degree by choosing either a simpler model, usually with less processes and/or parameters, or a more complex one. Selecting the best parametrization of the system behavior is of high relevance for engineers and scientists dealing with flow predictions. Indeed, it is so far not clear how to choose the optimal structural complexity for less biased and computationally-feasible predictions. The goals of this study are i) to statistically assess the reduction of structural uncertainty when increasing model complexity and ii) to select optimal model configuration considering the width of the predictive uncertainty intervals, magnitude

Druh

O - Ostatní výsledky

CEP obor

DA - Hydrologie a limnologie

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů