| dc.description.abstract | When using a hydrological model to estimate the amount of available
resources, the accuracy of the estimates depends on the calibration
of the model. That is, one needs to nd appropriate values for the
model parameters. Calibration of hydrological models requires the exploration
of a signi cant search space, rendering traditional gradient
descent techniques sub-optimal. The Bayesian learning automaton has
emerged as a simple and computationally e cient addition to current,
largely evolutionary, calibration techniques. Although particularly well
suited for learning in stochastic environments, the automaton struggles
with navigating huge action spaces.
To alleviate this limitation, we introduce a hierarchically structured
variant of the Bayesian learning automaton, applying it to the eld of
model calibration and function optimization. Several variants of the
automaton is implemented and empirically tested, as well as compared
to competing calibration techniques from the literature.
The new hierarchically structured automaton shows great promise, improving
on action space handling compared to earlier, non-hierarchical
structures. Indeed, the computational complexity now grows logarithmically
rather than linearly with the size of the action space. Our
experiments show that this approach is a viable alternative to competing
calibration techniques. | en_US |
