Using Hybrid Genetic Algorithms in Assembling Master Recession Curves of Karst Springs

Computation methods, using the evolution algorithm concept—mainly group of optimization methods known as genetic algorithms—provide an interesting methodical approach, applicable for a construction (not interpretation) of assembled recession curves. Assemblage of the master recession curve from various hydrograph segments by the help of the hybridized genetic algorithm approach can avoid obstacles such as limited time-series datasets, incomplete recessions or too many segments in many recession curves, complicated hydrograph shape in the case of karstic springs (caused by combined laminar and turbulent discharge sub-regimes due to karst network settings), different time intervals of observations (daily or weekly frequencies), short time-series intervals, imprecise measurements, different types of datasets (averaged or directly measured data) or even rough (inaccurate) measurements of discharges. The only type of discharge curve without practical output solution of the method presented is the constant outflow. In practice, assembling of recession curves is necessary for hydrogeological phenomena, especially in moderate climate regions, where recessional periods are frequently interrupted by partial recharge events. The presented algorithm was already implemented to a programme solution, so that the applicability of the hybrid genetic algorithms method for master recession curve creation is at hand without requirements on programming skills of hydrologists involved, which can be considered as an immediate advantage of the method described.