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Model Calibration with Hierarchically Structured Bayesian Learning Automata

Andreassen, Jan Gunnar; Engedal, Lars Magne
Master thesis
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IKT590 2011 spring Master thesis Jan Gunnar Andreassen.pdf (1.014Mb)
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http://hdl.handle.net/11250/137537
Issue date
2011
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  • Master's theses in Information and Communication Technology [352]
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.
Description
Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2011– Universitetet i Agder, Grimstad
Publisher
Universitetet i Agder / University of Agder

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