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

Result code in 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>
Alternative codes found
RIV/00216305:26110/21:PU142831
Result on the web
<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>

Result language
angličtina
Original language name
Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10501 - Hydrology

Project
<a href="/en/project/VI20192021166" target="_blank" >VI20192021166: Hydrometeorological risks in the Czech Republic - changes and prediction enhancements</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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