About: Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast (en)
skos:prefLabel	Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast (en)
skos:notation	RIV/60460709:41330/14:64707!RIV15-MSM-41330___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...iv/cisloPeriodika	78235
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Pavlásek, Jiří Pech, Pavel Máca, Petr
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Česká zemědělská univerzita v Praze / Fakulta životního prostředí
http://linked.open...dnocenehoVysledku	7992
http://linked.open...ai/riv/idVysledku	RIV/60460709:41330/14:64707
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Algorithms, Catchments, Chemical activation, Flood control, Floods, Neural networks, Optimization, Runoff, Transfer functions Activation functions, Artificial neural network models, Headwater catchment, Hidden layer neurons, Levenberg-Marquardt algorithm, Local optimization algorithm, Local optimization methods, Nonlinear transfer functions (en)
http://linked.open.../riv/klicoveSlovo	Flood control Floods Optimization Catchments Algorithms Neural networks Runoff Levenberg-Marquardt algorithm Artificial neural network models Chemical activation Headwater catchment Hidden layer neurons Local optimization algorithm Local optimization methods Nonlinear transfer functions Transfer functions Activation functions
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[1FB1B506B354]
http://linked.open...i/riv/nazevZdroje	MATHEMATICAL PROBLEMS IN ENGINEERING
http://linked.open...in/vavai/riv/obor	DA
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	2014
http://linked.open...iv/tvurceVysledku	Máca, Petr Pavlásek, Jiří Pech, Pavel
http://linked.open...ain/vavai/riv/wos	0003389191
issn	1024-123X
number of pages	10 (xsd:int)
http://localhost/t...ganizacniJednotka	41330

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