A Hybrid Approach for Predicting River Runoff

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096537" target="_blank" >RIV/61989100:27240/15:86096537 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86096537 RIV/61989100:27730/15:86096537

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-21206-7_6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-21206-7_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-21206-7_6" target="_blank" >10.1007/978-3-319-21206-7_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Hybrid Approach for Predicting River Runoff

Popis výsledku v původním jazyce

Time series prediction has attracted attention of many researchers as well as practitioners from different fields and many approaches have been proposed. Traditionally, sliding window technique was employed to transform data first and then some learningmodels such as fuzzy neural networks were exploited for prediction. In order to improve the prediction performance, we propose an approach that combines chaotic theory, recurrent fuzzy neural network (RFNN), and K-means. In the past few decades, fuzzy neural networks have been proven to be a great method for modeling, characterizing and predicting many kinds of nonlinear hydrology time series data such as rainfall, water quality, and river runoff. Chaotic theory is a field of physics and mathematics, and having been used to solve many practical problems emerging from industrial practices. In our proposed approach, chaotic theory is firstly exploited to transformoriginal data to a new kind of data called phase space. Then, a novel hybrid

Název v anglickém jazyce

A Hybrid Approach for Predicting River Runoff

Popis výsledku anglicky

Time series prediction has attracted attention of many researchers as well as practitioners from different fields and many approaches have been proposed. Traditionally, sliding window technique was employed to transform data first and then some learningmodels such as fuzzy neural networks were exploited for prediction. In order to improve the prediction performance, we propose an approach that combines chaotic theory, recurrent fuzzy neural network (RFNN), and K-means. In the past few decades, fuzzy neural networks have been proven to be a great method for modeling, characterizing and predicting many kinds of nonlinear hydrology time series data such as rainfall, water quality, and river runoff. Chaotic theory is a field of physics and mathematics, and having been used to solve many practical problems emerging from industrial practices. In our proposed approach, chaotic theory is firstly exploited to transformoriginal data to a new kind of data called phase space. Then, a novel hybrid

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in intelligent systems and computing. Volume 370

ISBN

978-3-319-21205-0

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

61-71

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Ostrava

Datum konání akce

29. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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