Detail publikace

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

KOZEL, T. VLASÁK, T. JANÁL, P.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

hydrological forecast; floods; artificial intelligence methods; post-processing

Autoři

KOZEL, T.; VLASÁK, T.; JANÁL, P.

Vydáno

8. 7. 2021

Nakladatel

MDPI

Místo

Basel, Switzerland

ISSN

2073-4441

Periodikum

Water

Ročník

13

Číslo

14

Stát

Švýcarská konfederace

Strany od

1

Strany do

15

Strany počet

15

URL

Plný text v Digitální knihovně

BibTex

@article{BUT175685,
  author="Tomáš {Kozel} and Tomáš {Vlasák} and Petr {Janál}",
  title="Possibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecasts",
  journal="Water",
  year="2021",
  volume="13",
  number="14",
  pages="1--15",
  doi="10.3390/w13141894",
  issn="2073-4441",
  url="https://www.mdpi.com/2073-4441/13/14/1894"
}