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4 CONCLUSIONS AND FUTURE
WORK
In this paper we highlight the necessity to
incorporate the temporal measures in studying the
evolution of geographical objects over the time.
In order to achieve this objective we proposed an
approach aiming to extract knowledge from a
spatiotemporal database by the means of
spatiotemporal mining queries merging both data
mining and time management concepts.
As future work, the most meaningful rules will
be processed by a learning system (e.g. neural
network) in order to identify unknown future risks.
We will also evaluate the possibility to
implement our approach in a wider range of risk
prediction applications including appropriate input
parameters suitable for the study region and the kind
of risk to predict.
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