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Statements

Subject Item
n2:RIV%2F60460709%3A41330%2F13%3A66712%21RIV15-MSM-41330___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
This paper focuses on improving rainfall-runoff forecasts by a combination of genetic programming (GP) and basic hydrological modelling concepts. GP is a general optimisation technique formaking an automated search of a computer program that solves some particular problem. The SORD! program was developed for the purposes of this study (in the R programming language). It is an implementation of canonical GP. Special functions are used for a combined approach of hydrological concepts and GP. The special functions are a reservoir model, a simple moving average model, and a cumulative sum and delay operator. The efficiency of the approach presented here is tested on runoff predictions for five catchments of various sizes. The input data consists of daily rainfall and runoff series. The forecast step is one day. The performance of the proposed approach is compared with the results of the artificial neural network model (ANN) and with the GP model without special functions. GP combined with these This paper focuses on improving rainfall-runoff forecasts by a combination of genetic programming (GP) and basic hydrological modelling concepts. GP is a general optimisation technique formaking an automated search of a computer program that solves some particular problem. The SORD! program was developed for the purposes of this study (in the R programming language). It is an implementation of canonical GP. Special functions are used for a combined approach of hydrological concepts and GP. The special functions are a reservoir model, a simple moving average model, and a cumulative sum and delay operator. The efficiency of the approach presented here is tested on runoff predictions for five catchments of various sizes. The input data consists of daily rainfall and runoff series. The forecast step is one day. The performance of the proposed approach is compared with the results of the artificial neural network model (ANN) and with the GP model without special functions. GP combined with these
dcterms:title
Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting
skos:prefLabel
Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting
skos:notation
RIV/60460709:41330/13:66712!RIV15-MSM-41330___
n4:aktivita
n15:S
n4:aktivity
S
n4:cisloPeriodika
1
n4:dodaniDat
n6:2015
n4:domaciTvurceVysledku
n8:9176802 n8:5971845 n8:4539699 n8:5102065 n8:5125561
n4:druhVysledku
n17:J
n4:duvernostUdaju
n13:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
21142
n4:idVysledku
RIV/60460709:41330/13:66712
n4:jazykVysledku
n16:eng
n4:klicovaSlova
Evolutionary algorithms, Genetic programming, Hydrology, Rainfall-runoff modelling, Runoff forecast
n4:klicoveSlovo
n10:Genetic%20programming n10:Evolutionary%20algorithms n10:Rainfall-runoff%20modelling n10:Runoff%20forecast n10:Hydrology
n4:kodStatuVydavatele
CZ - Česká republika
n4:kontrolniKodProRIV
[0C50304CB0E1]
n4:nazevZdroje
COMPUTING
n4:obor
n5:DA
n4:pocetDomacichTvurcuVysledku
5
n4:pocetTvurcuVysledku
5
n4:rokUplatneniVysledku
n6:2013
n4:svazekPeriodika
95
n4:tvurceVysledku
Máca, Petr Pech, Pavel Hanel, Martin Havlíček, Vojtěch Kuráž, Michal
n4:wos
000338630100021
s:issn
0010-485X
s:numberOfPages
18
n12:organizacniJednotka
41330