DDDAS approaches to wildland fire modeling and contaminant tracking

Craig C. Douglas, Robert A. Lodder, Richard E. Ewing, Yalchin Efendiev, Guan Qin, Janice Coen, Mauricio Kritz, Jonathan D. Beezley, Jan Mandel, Mohamed Iskandarani, Anthony Vodacek, Gundolf Haase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations


We report on two ongoing efforts to build Dynamic Data Driven Application Systems (DDDAS) for (1) short-range forecasting of weather and wildfire behavior from real time weather data, images, and sensor streams, and (2) contaminant identification and tracking in water bodies. Both systems change their forecasts as new data is received. We use one long term running simulation that self corrects using out of order, imperfect sensor data. The DDDAS versions replace codes that were previously run using data only in initial conditions. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process.

Original languageEnglish (US)
Title of host publicationProceedings - Winter Simulation Conference
Number of pages8
StatePublished - 2006
Event2006 Winter Simulation Conference, WSC - Monterey, CA, United States
Duration: Dec 3 2006Dec 6 2006


Other2006 Winter Simulation Conference, WSC
CountryUnited States
CityMonterey, CA


ASJC Scopus subject areas

  • Engineering(all)

Cite this

Douglas, C. C., Lodder, R. A., Ewing, R. E., Efendiev, Y., Qin, G., Coen, J., Kritz, M., Beezley, J. D., Mandel, J., Iskandarani, M., Vodacek, A., & Haase, G. (2006). DDDAS approaches to wildland fire modeling and contaminant tracking. In Proceedings - Winter Simulation Conference (pp. 2117-2124). [4117859] https://doi.org/10.1109/WSC.2006.323011