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| Titel |
Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean |
| VerfasserIn |
Anke Scherb, Panagiota Papakosta, Daniel Straub |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250088860
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| Publikation (Nr.) |
 EGU/EGU2014-3035.pdf |
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| Zusammenfassung |
| Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the
Mediterranean. To facilitate coping with wildfire risks, an understanding of the
factors influencing wildfire occurrence and behavior (e.g. human activity, weather
conditions, topography, fuel loads) and their interaction is of importance, as is the
implementation of this knowledge in improved wildfire hazard and risk prediction
systems.
In this project, a probabilistic wildfire risk prediction model is developed, with integrated
fire occurrence and fire propagation probability and potential impact prediction on natural
and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic
modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1
km2 spatial and 1 day temporal resolution). The modeled consequences account for potential
restoration costs and production losses referred to forests, agriculture, and (semi-) natural
areas. BNs and a geographic information system (GIS) are coupled within this project to
support a semi-automated BN model parameter learning and the spatial-temporal risk
prediction. The coupling also enables the visualization of prediction results by means of daily
maps.
The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is
used as validation data set. A special focus is put on the performance evaluation of the BN for
fire occurrence, which is modeled as binary classifier and thus, could be validated by means
of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of
more than 70% for validation could be achieved, which indicates potential for reliable
prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final
prediction results.
The resulting system can be easily expanded to predict additional expected
damages in the mesoscale (e.g. building and infrastructure damages). The system
can support planning of preventive measures (e.g. state resources allocation for
wildfire prevention and preparedness) and assist recuperation plans of damaged areas. |
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