Publication detail

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

KOZEL, T. VLASÁK, T. JANÁL, P.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

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

Authors

KOZEL, T.; VLASÁK, T.; JANÁL, P.

Released

8. 7. 2021

Publisher

MDPI

Location

Basel, Switzerland

ISBN

2073-4441

Periodical

Water

Year of study

13

Number

14

State

Swiss Confederation

Pages from

1

Pages to

15

Pages count

15

URL

Full text in the Digital Library

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"
}