Building a Dynamic Data Driven Application System for Hurricane Forecasting
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
A Dynamic Data Driven Wildland Fire Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Support for Urgent Computing Based on Resource Virtualization
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Injecting Dynamic Real-Time Data into a DDDAS for Forest Fire Behavior Prediction
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Half-duplex dynamic data driven application system for forest fire spread prediction
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
Input parameter calibration in forest fire spread prediction: taking the intelligentway
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
(OBIFS) isotropic image analysis for improving a predicting agent based systems
Expert Systems with Applications: An International Journal
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This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior.