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Article

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

by 1,2,*, 3 and 1
1
Czech Hydrometeorological Institute, Kroftova 43, 616 00 Brno, Czech Republic
2
Faculty of Civil Engineering, Institute of Landscape Water Management, Brno University of Technology, Veveří 331/95, 602 00 Brno, Czech Republic
3
Czech Hydrometeorological Institute, A. Staška 1177, Rožnov, 370 07 České Budějovice, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Franchini
Water 2021, 13(14), 1894; https://doi.org/10.3390/w13141894
Received: 11 June 2021 / Revised: 29 June 2021 / Accepted: 6 July 2021 / Published: 8 July 2021
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. View Full-Text
Keywords: hydrological forecast; floods; artificial intelligence methods; post-processing hydrological forecast; floods; artificial intelligence methods; post-processing
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MDPI and ACS Style

Kozel, T.; Vlasak, T.; Janal, P. Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts. Water 2021, 13, 1894. https://doi.org/10.3390/w13141894

AMA Style

Kozel T, Vlasak T, Janal P. Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts. Water. 2021; 13(14):1894. https://doi.org/10.3390/w13141894

Chicago/Turabian Style

Kozel, Tomas, Tomas Vlasak, and Petr Janal. 2021. "Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts" Water 13, no. 14: 1894. https://doi.org/10.3390/w13141894

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