Application of random number generators in genetic algorithms to improve rainfall-runoff modelling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985874%3A_____%2F17%3A00480548" target="_blank" >RIV/67985874:_____/17:00480548 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/17:00312740

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jhydrol.2017.08.025" target="_blank" >http://dx.doi.org/10.1016/j.jhydrol.2017.08.025</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jhydrol.2017.08.025" target="_blank" >10.1016/j.jhydrol.2017.08.025</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of random number generators in genetic algorithms to improve rainfall-runoff modelling

Popis výsledku v původním jazyce

The efficient calibration of rainfall-runoff models is a difficult issue, even for experienced hydrologists. Therefore, fast and high-quality model calibration is a valuable improvement. This paper describes a novel methodology and software for the optimisation of a rainfall-runoff modelling using a genetic algorithm (GA) with a newly prepared concept of a random number generator (HRNG), which is the core of the optimisation. The GA estimates model parameters using evolutionary principles, which requires a quality number generator. The new HRNG generates random numbers based on hydrological information and it provides better numbers compared to pure software generators. The GA enhances the model calibration very well and the goal is to optimise the calibration of the model with a minimum of user interaction. This article focuses on improving the internal structure of the GA, which is shielded from the user. The results that we obtained indicate that the HRNG provides a stable trend in the output quality of the model, despite various configurations of the GA. In contrast to previous research, the HRNG speeds up the calibration of the model and offers an improvement of rainfall-runoff modelling.

Název v anglickém jazyce

Application of random number generators in genetic algorithms to improve rainfall-runoff modelling

Popis výsledku anglicky

The efficient calibration of rainfall-runoff models is a difficult issue, even for experienced hydrologists. Therefore, fast and high-quality model calibration is a valuable improvement. This paper describes a novel methodology and software for the optimisation of a rainfall-runoff modelling using a genetic algorithm (GA) with a newly prepared concept of a random number generator (HRNG), which is the core of the optimisation. The GA estimates model parameters using evolutionary principles, which requires a quality number generator. The new HRNG generates random numbers based on hydrological information and it provides better numbers compared to pure software generators. The GA enhances the model calibration very well and the goal is to optimise the calibration of the model with a minimum of user interaction. This article focuses on improving the internal structure of the GA, which is shielded from the user. The results that we obtained indicate that the HRNG provides a stable trend in the output quality of the model, despite various configurations of the GA. In contrast to previous research, the HRNG speeds up the calibration of the model and offers an improvement of rainfall-runoff modelling.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Journal of Hydrology

ISSN

0022-1694

e-ISSN

—

Svazek periodika

553

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

350-355

Kód UT WoS článku

000412612700027

EID výsledku v databázi Scopus

2-s2.0-85027697238