A Stochastic Hybrid Optimization Algorithm to Calibration Conceptual Hydrologic Model Parameters

Combining the approximate gradient-based steepest descent algorithm and the pattern search algorithm the author present GP algorithm, a new local optimization algorithm for conceptual hydrologic model parameters. with Nash facticity coefficient as the target function the random search techniques was used for search parameter space, then optimize the selected parameter set using GP algorithm. The global optimization parameter is achieved by filtering parameter space strategy. The method above-mentioned comprise the derivative information and stochastic properties, make the optimization set escape the local maximum to the global set. The practical efficiency was verified by using a case in Yang Lou unite drainage basin. It was shown that parameters of hydrologic model can successfully be automatically calibrated.