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

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dc.contributor.authorKozel, Tomášcs
dc.contributor.authorVlasák, Tomášcs
dc.contributor.authorJanál, Petrcs
dc.coverage.issue14cs
dc.coverage.volume13cs
dc.date.accessioned2022-01-20T15:56:07Z
dc.date.available2022-01-20T15:56:07Z
dc.date.issued2021-07-08cs
dc.description.abstractWhen 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.en
dc.formattextcs
dc.format.extent1-15cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationWater. 2021, vol. 13, issue 14, p. 1-15.en
dc.identifier.doi10.3390/w13141894cs
dc.identifier.issn2073-4441cs
dc.identifier.other175685cs
dc.identifier.urihttp://hdl.handle.net/11012/203354
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofWatercs
dc.relation.urihttps://www.mdpi.com/2073-4441/13/14/1894cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2073-4441/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjecthydrological forecasten
dc.subjectfloodsen
dc.subjectartificial intelligence methodsen
dc.subjectpost-processingen
dc.titlePossibilities of Using Neuro-Fuzzy Models for Post-Processing of Hydrological Forecastsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-175685en
sync.item.dbtypeVAVen
sync.item.insts2022.03.05 20:54:37en
sync.item.modts2022.03.05 20:15:56en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav vodního hospodářství krajinycs
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