Applying recurrent fuzzy neural network to predict the Runoff of Srepok River

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86093037" target="_blank" >RIV/61989100:27240/14:86093037 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-662-45237-0_7" target="_blank" >http://dx.doi.org/10.1007/978-3-662-45237-0_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-662-45237-0_7" target="_blank" >10.1007/978-3-662-45237-0_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Applying recurrent fuzzy neural network to predict the Runoff of Srepok River

Popis výsledku v původním jazyce

Recurrent fuzzy neural network (RFNN) is proven to be a great method for modeling, characterizing and predicting many kinds of nonlinear hydrological time series data such as rainfall, water quality, and river runoff. In our study, we employed RFNN to find out the correlation between the climate data and the runoff of Srepok River in Vietnam and then to model and predict the runoff of Srepok River in the current, as well as in the future. In order to prove the advantage of RFNN, we compare RFNN with anenvironmental model called SWAT on the same dataset. We conduct experiments using the climate data and the daily river's runoff data that have been collected in 22 years, ranging from 1900 to 2011. The experiment results show that the relative error of RFNN is about 0.35 and the relative error of SWAT is 0.44. It means that RFNN outperforms SWAT. Moreover, the most important advantage of RFNN when comparing with SWAT is that RFNN does not need much data as SWAT does. IFIP International F

Název v anglickém jazyce

Applying recurrent fuzzy neural network to predict the Runoff of Srepok River

Popis výsledku anglicky

Recurrent fuzzy neural network (RFNN) is proven to be a great method for modeling, characterizing and predicting many kinds of nonlinear hydrological time series data such as rainfall, water quality, and river runoff. In our study, we employed RFNN to find out the correlation between the climate data and the runoff of Srepok River in Vietnam and then to model and predict the runoff of Srepok River in the current, as well as in the future. In order to prove the advantage of RFNN, we compare RFNN with anenvironmental model called SWAT on the same dataset. We conduct experiments using the climate data and the daily river's runoff data that have been collected in 22 years, ranging from 1900 to 2011. The experiment results show that the relative error of RFNN is about 0.35 and the relative error of SWAT is 0.44. It means that RFNN outperforms SWAT. Moreover, the most important advantage of RFNN when comparing with SWAT is that RFNN does not need much data as SWAT does. IFIP International F

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 8838

ISBN

978-3-662-45236-3

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

55-66

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Ho Chi Minh City

Datum konání akce

5. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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