Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F14%3A64707" target="_blank" >RIV/60460709:41330/14:64707 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast
Popis výsledku v původním jazyce
The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
Název v anglickém jazyce
Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast
Popis výsledku anglicky
The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
DA - Hydrologie a limnologie
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2014
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
MATHEMATICAL PROBLEMS IN ENGINEERING
ISSN
1024-123X
e-ISSN
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Svazek periodika
2014
Číslo periodika v rámci svazku
78235
Stát vydavatele periodika
CZ - Česká republika
Počet stran výsledku
10
Strana od-do
1-10
Kód UT WoS článku
0003389191
EID výsledku v databázi Scopus
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