Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F21%3AN0000010" target="_blank" >RIV/00020699:_____/21:N0000010 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26110/21:PU142831

Výsledek na webu

<a href="https://www.mdpi.com/2073-4441/13/14/1894/htm" target="_blank" >https://www.mdpi.com/2073-4441/13/14/1894/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/w13141894" target="_blank" >10.3390/w13141894</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts

Popis výsledku v původním jazyce

When issuing hydrological forecasts and warnings for individual profiles, the aim is to achieve the best possible results. Hydrological forecasts themselves are burdened by an error (uncertainty) at the inputs (precipitation forecast) as well as on the side of the hydrological model used. The aim of the method described in this article is to reduce the error of the hydrological model using post-processing the model results. Models based on neuro-fuzzy models were selected for the post-processing itself. The whole method was tested on 12 profiles in the Czech Republic. The catchment size of the individual profiles ranged from 90 to 4500 km2 and the profiles varied in their character, both in terms of elevation as well as land cover. After finding the suitable model architecture and introducing supporting algorithms, there was an improvement in the results for the individual profiles for selected criteria by on average 5–60% (relative culmination error, mean square error) compared to the results of re-simulation of the hydrological model. The results of the application show that the method was able to improve the accuracy of hydrological forecasts and thus could contribute to better management of flood situations.

Název v anglickém jazyce

Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts

Popis výsledku anglicky

When issuing hydrological forecasts and warnings for individual profiles, the aim is to achieve the best possible results. Hydrological forecasts themselves are burdened by an error (uncertainty) at the inputs (precipitation forecast) as well as on the side of the hydrological model used. The aim of the method described in this article is to reduce the error of the hydrological model using post-processing the model results. Models based on neuro-fuzzy models were selected for the post-processing itself. The whole method was tested on 12 profiles in the Czech Republic. The catchment size of the individual profiles ranged from 90 to 4500 km2 and the profiles varied in their character, both in terms of elevation as well as land cover. After finding the suitable model architecture and introducing supporting algorithms, there was an improvement in the results for the individual profiles for selected criteria by on average 5–60% (relative culmination error, mean square error) compared to the results of re-simulation of the hydrological model. The results of the application show that the method was able to improve the accuracy of hydrological forecasts and thus could contribute to better management of flood situations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10501 - Hydrology

Projekt

<a href="/cs/project/VI20192021166" target="_blank" >VI20192021166: Hydrometeorologická rizika v České republice - změny rizik a zlepšení jejich predikcí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Water

ISSN

2073-4441

e-ISSN

2073-4441

Svazek periodika

13

Číslo periodika v rámci svazku

1894

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

000677112200001

EID výsledku v databázi Scopus

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