Uncertainty in parameters are expressed as ranges (uniform distributions). The algorithm accounts for all sources of uncertainties (driving variables, conceptual model, parameters and measured data.


After defining the generalized likelihood measure, a large number of parameter sets are randomly sampled from the prior distribution and each parameter set is assessed as either behavioral or non-behavioral through a comparison of the likelihood measure with the given threshold value. Then, each behavioral parameter is given a likelihood weight. Finally, the uncertainty is predicted.


This algorithm represents a population based stochastic optimization technique. It is initialized with a group of random particles (solutions) and then searches for optima by updating generations.


The PARASOL method uses objective functions (OF) into a global optimization criterion (GOC), minimizes these OF or GOC using the Shuffle Complex (SCE-UA) algorithm and performs uncertainty analysis.


MCMC generates samples from a random walk which adapts to the posterior distribution