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.