Year: 2012
Author: A. Cencerrado, R. Rodríguez, A. Cortes, T. Margalef
International Journal of Numerical Analysis and Modeling, Vol. 9 (2012), Iss. 2 : pp. 432–448
Abstract
This work faces the problem of quality and prediction time assessment in a Dynamic
Data Driven Application System (DDDAS) for predicting natural hazard evolution. Natural
hazard management is undoubtedly a relevant area where systems modeling and numerical analysis
take a great prominence.
Modeling such systems is a very hard problem to tackle. Besides, the results obtained by
simulators usually don't provide accurate information, mostly due to the underlying uncertainty
in the input parameters that define the actual environmental conditions at the very beginning of
the simulation.
For this reason, we have developed a two-stage prediction strategy, which, first of all, carries
out a parameter adjustment process by comparing the results provided by the simulator and the
real observed hazard evolution. It has been demonstrated that this method improves notably the
quality of the predictions. Furthermore, we have designed data injection techniques that allow
us to take advantage from real-time acquired information, so that our strategy fits the DDDAS
paradigm.
Nevertheless, because of the urgent nature of the systems we deal with, it is also necessary to
assess the time incurred in applying the above mentioned strategy, in order for it to be useful and
applicable in a real emergency situation. In this sense, we have developed a new methodology
for prediction time assessment under this kind of prediction environments, based on Artificial
Intelligence techniques.
In this research work, we have chosen forest fires as a representative study case, although the
exposed methods can be extrapolated to any kind of natural hazard.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2012-IJNAM-640
International Journal of Numerical Analysis and Modeling, Vol. 9 (2012), Iss. 2 : pp. 432–448
Published online: 2012-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 17
Keywords: DDDAS Data Uncertainty Natural Hazard Management Prediction Quality Prediction Time Assessment.