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| Titel |
A Vulcano Expert System: Automatic Alert Level Estimation and GIS Visualization on top of a multi-parameter Data Base |
| VerfasserIn |
Moritz Beyreuther, Robert Barsch, Stefan Bernsdorf, Klemen Zakšek, Joachim Wassermann |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250035494
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| Zusammenfassung |
| The success of volcano fast response systems including early warning of an imminent
eruption lives through its connection capability to already installed monitoring systems. Also
new temporary, possible wireless networks as well as combination of different raw and model
data needs to be covered. In practice this means a high-dimensional, complicated (raw or
already parameterised) data stream with different sampling rates and time histories that have
to be stored and analysed.
In the framework of the Exupery project (GEOTECHNOLOGIEN, German Ministry for
Education and Research - BMBF) the SeisHub Database handles multi-parameter data
resulting from modern volcano monitoring networks simultaneously. This is a quite common
situation today in order to assess volcanic activity.
The warning system, here a GIS and an automatic alert level estimation, connects to the
data base to retrieve the relevant information. The GIS provides access to the data as well as
analysis results. By overlaying various parameters in the GIS systems the expert can analyse
the situation and base his/her decision easily on multi-parameter data. In addition to data
interactive visualization via the GIS the alert level of the volcano is automatically estimated
using a Bayesian Belief Network (BBN) approach. This allows the expert to verify his own
assessment versus the automatic system. In case there are major differences, the expert can
identify the origin of the difference in the graphical representation of the BBN and if
necessary adapt the BBN. BBNs are chosen because of their transparency (graphical
representation), flexibility, probabilistic architecture and their possibility to incorporate expert
knowledge. The probabilistic architecture allows to compute a confidence measure for the
given alert level. A high, automatically estimated alert level with either high or
low confidence can certainly lead to different decisions by the human interpreter. |
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