| Summary: | Time series analysis requires powerful and robust tools; at the same time the tools must be intuitive for users. Bayesian networks have been widely applied in static problem modelling, but, in some knowledge areas, Dynamic
Bayesian networks are hardly known. Such is the case in the environmental sciences, where the application of static Bayesian networks in water resources research is notable, while fewer than five papers have been found in the literature for the dynamic extension. The aim of this paper is to show how Dynamic Bayesian networks can be applied in environmental sciences by means of a case study in water reservoir system management. Two approaches are applied and compared for model learning, and another two for inference. Despite slight differences in terms of model complexity and computational time, both approaches for model learning provide similar results. In the case of inference methods, again, there were slight differences in computational time,
but the selection of one approach over the other is based on the prediction needed: If the aim is just to go one step forward, both Window and Roll out approaches are similar, when we need to go more than one step forward; the most appropriate will be Roll out.
|
|---|