Abstract
Predicting the potential danger of a forest fire is an essential task of wildfire analysts. For that reason, many scientists have focused their efforts on developing propagation models that predict forest fire evolution to mitigate the consequences of such hazards. These propagation models require a precise knowledge of the whole environment where the fire is taking place. In the context of natural hazards simulation, it is well known that, part of the final forecast error comes from the uncertainty in the input data. In this work, we use a Dynamic Data-driven methodology to overcome such problem. The core of the methodology is a calibration stage previous to the forecast where complementary models, data injection and intelligent systems are working in a symbiotic way to reduce the forecast errors at real time. This approach has been tested using a forest fire that took place in Arkadia (Greece) in 2011.
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References
Rothermel, R.C.: Behave and you can predict fire behavior. Fire Manag. Notes 44(4), 11–15 (1983)
Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C.: A simulation of probabilistic wildfire risk components for the continental united states. Stoch. Env. Res. Risk Assess. 25(7), 973–1000 (2011)
Andrews, P.L., Bevins, C.D., Seli, R.C., et al.: BehavePlus fire modeling system: version 2.0: user’s guide. US Department of Agriculture (Forest Service), Rocky Mountain Research Station Fort Collins (2003)
Finney, M.A., et al.: An overview of flammap fire modeling capabilities. In: Proceedings of the Fuels Management-How to Measure Success, pp. 213–220 (2006)
Bevins, C.D.: Firelib user manual and technical reference (1996)
Finney, M.A., et al.: Farsite, fire area simulator-model development and evaluation (2004)
Lopes, A., Cruz, M., Viegas, D.: Firestationan integrated software system for the numerical simulation of fire spread on complex topography. Environ. Model. Softw. 17(3), 269–285 (2002)
Rothermel, R.C.: A mathematical model for predicting fire spread in wildland fuels. Director (INT-115), 40 p. (1972)
Cencerrado, A., Rodríguez, R., Cortés, A., Margalef, T.: Urgency versus accuracy: dynamic driven application system natural hazard management. Int. J. Numer. Anal. Model. 9, 432–448 (2012)
Abdalhaq, B., Cortés, A., Margalef, T., Luque, E.: Enhancing wildland fire prediction on cluster systems applying evolutionary optimization techniques. Future Gener. Comput. Syst. 21(1), 61–67 (2005)
Bianchini, G., Denham, M., Cortés, A., Margalef, T., Luque, E.: Wildland fire growth prediction method based on multiple overlapping solution. J. Comput. Sci. 1(4), 229–237 (2010)
Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004)
Darema, F.: Grid computing and beyond: the context of dynamic data driven applications systems. Proc. IEEE 93(3), 692–697 (2005)
Potter, B., Butler, B.: Using wind models to more effectively manage wildfire. Fire Manage. 69(2), 40–46 (2009)
Group, W.W.: Weather Research and Forecasting (WRF) model. Director (INT-115) (2008)
Molteni, F., Buizza, R., Palmer, T.N., Petroliagis, T.: The ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc. 122(529), 73–119 (1996)
Bennett, N.D., Croke, B.F., Guariso, G., Guillaume, J.H., Hamilton, S.H., Jakeman, A.J., Marsili-Libelli, S., Newham, L.T., Norton, J.P., Perrin, C., et al.: Characterising performance of environmental models. Environ. Model. Softw. 40, 1–20 (2013)
Acknowledgements
This research has been supported by MICINN-Spain under contract TIN2011-28689-C02-01 and by the Catalan Government under grant 2014-SGR-576. We also want to thank the member of EFFIS team at the JRC (Ispra) for their valuable collaboration.
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Brun, C., Cortés, A., Margalef, T. (2015). Coupled Dynamic Data-Driven Framework for Forest Fire Spread Prediction. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_6
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DOI: https://doi.org/10.1007/978-3-319-25138-7_6
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