<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The article describes a new methodology of using genetic algorithms to assemble a natural time series of discharge recession, from which a master recession curve can be interpreted both for streams and for springs. Presented approach can avoid obstacles such as limited time-series datasets, incomplete recessions or too many recessionary segments in many recession series, different time intervals of observations (daily or weekly frequencies). Short time-series intervals, imprecise or mistaken measurements and different types of datasets (averaged or directly measured data) are taken into account as well. Even rough measurements of discharges with inaccurate sensing range can be analysed, if sufficiently long observation is available. Complicated hydrograph shapes in the case of e.g. karstic springs (often caused by combination of laminar and turbulent discharge sub-regimes due to karst network settings) can be processed as well. Subsequent construction of master recession curve is much easier an offers better conditions for its interpretation. Presented algorithm was already implemented to a programme solution, completed on the user form.</p></abstract>

The article describes a new methodology of using genetic algorithms to assemble a natural time series of discharge recession, from which a master recession curve can be interpreted both for streams and for springs. Presented approach can avoid obstacles such as limited time-series datasets, incomplete recessions or too many recessionary segments in many recession series, different time intervals of observations (daily or weekly frequencies). Short time-series intervals, imprecise or mistaken measurements and different types of datasets (averaged or directly measured data) are taken into account as well. Even rough measurements of discharges with inaccurate sensing range can be analysed, if sufficiently long observation is available. Complicated hydrograph shapes in the case of e.g. karstic springs (often caused by combination of laminar and turbulent discharge sub-regimes due to karst network settings) can be processed as well. Subsequent construction of master recession curve is much easier an offers better conditions for its interpretation. Presented algorithm was already implemented to a programme solution, completed on the user form.

Construction of Master Recession Curve Using Genetic Algorithms

Department of Hydrogeology and Geothermal Energy, State Geological Institute of Dionýz Štúr - Geological Survey of Slovak Republic, Mlynská dolina 1, 817 04 Bratislava, Slovakia; Mailto

Department of Hydrogeology, Faculty of Natural Sciences - Comenius University, Mlynská dolina, 842 15 Bratislava, Slovakia

Journal of Hydrology and Hydromechanics

{"article-title":"Construction of Master Recession Curve Using Genetic Algorithms"}

The article describes a new methodology of using genetic algorithms to assemble a natural time series of discharge recession, from which a master recession...

Construction of Master Recession Curve Using Genetic Algorithms

Publicado en línea: 06 mar 2012

Páginas: 3 - 15

DOI: https://doi.org/10.2478/v10098-012-0001-8

Palabras clave
analýza výtokových čiar, reprezentatívna výtoková čiara, hybridné genetické algoritmy, evolučné algoritmy, HydroOffice

This content is open access.

Construction of Master Recession Curve Using Genetic Algorithms

Publicado en línea: 06 mar 2012

Páginas: 3 - 15

DOI: https://doi.org/10.2478/v10098-012-0001-8

Palabras claveanalýza výtokových čiar, reprezentatívna výtoková čiara, hybridné genetické algoritmy, evolučné algoritmy, HydroOffice

This content is open access.

Palabras clave
analýza výtokových čiar, reprezentatívna výtoková čiara, hybridné genetické algoritmy, evolučné algoritmy, HydroOffice