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Improving Disaster Resilience and Mitigation - IT Means and Tools pp 293–305Cite as

FireFight: A Decision Support System for Forest Fire Containment

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Abstract

The FireFight project is being developed in collaboration with the GRAF wildland firefighting department (Generalitat de Catalunya, Spain). The main objective is the development of a web-accessible decision support system based on an integrated simulation and optimization framework for optimal wildfire containment. FireFight uses the tooPath (www.toopath.com) web server infrastructure to acquire the broadcasted real-time GPS position of approximately 1,650 land and aerial firefighting resources deployed across the territory. The short-term goal of the project is to help managers in making decisions about the number of extinguishing teams that should be deployed, the design of the water supply chain to bring water and other supplies to the firefighting teams, and the design of the change-of-shift transportation problem.

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References

  1. Ascoli D, Lonati M, Marzano R, Bovio G, Cavallero A, Lombardi G (2013) Prescribed burning and browsing to control tree encroachment in southern European heathlands. For Ecol Manage 289:69–77. doi:10.1016/j.foreco.2012.09.041

    Article  Google Scholar 

  2. Syphard AD, Keeley JE, Brennan TJ (2011) Comparing the role of fuel breaks across southern California national forests. For Ecol Manage 261(11):2038–2048. doi:10.1016/j.foreco.2011.02.030

    Article  Google Scholar 

  3. Hu X, Ntaimo L (2009) Integrated simulation and optimization for wildfire containment. ACM Trans Model Comput Simul 19(4):1–29. doi:10.1145/1596519.1596524

    Article  Google Scholar 

  4. Andrews P (2007) BehavePlus fire modeling system: past, present, and future. … of 7th symposium on fire and forest meteorology. Retrieved from http://www.researchgate.net/publication/8400756_Diving_ability_of_Anopheles_gambiae_(Diptera_Culicidae)_larvae/file/9fcfd4ff51c851cff4.doc

  5. Innocenti E, Santucci J, Hill DRC, Cnrs IUMR, Cedex A (2004) Active-DEVS: a computational model for the simulation of forest fire propagation *

    Google Scholar 

  6. Rothermel RC (1983) How to predict the spread and intensity of forest and range fires. Boise, Idaho, p 166

    Google Scholar 

  7. Luo W, Taylor M, Parker S (2008) A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int J Climatol 959:947–959. doi:10.1002/joc

    Article  Google Scholar 

  8. Luo W, Taylor M, Parker S (2004) Spatial interpolation for wind data in England and Wales, University of York. doi:10.1002/joc.1583

  9. González JR, Palahí M, Trasobares A, Pukkala T (2006) A fire probability model for forest stands in Catalonia (north-east Spain). Annals For Sci 63(2):169–176. doi:10.1051/forest:2005109

    Article  Google Scholar 

  10. Hernández-Leal PA, González-Calvo A, Arbelo M, Barreto A, Alonso-Benito A (2008) Synergy of GIS and remote sensing data in forest fire danger modeling. IEEE J Sel Top Appl Earth Obs Remote Sens 1(4):240–247, Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4703188

    Article  Google Scholar 

  11. Pennypacker C, Jakubowski M, Kelly M, Lampton M, Schmidt C, Stephens S, Tripp R (2013) FUEGO – fire urgency estimator in geosynchronous orbit – a proposed early-warning fire detection system. Remote Sens 5(10):5173–5192. doi:10.3390/rs5105173

    Article  Google Scholar 

  12. Carvalheiro LC, Bernardo SO, Orgaz MDM, Yamazaki Y (2010) Forest fires mapping and monitoring of current and past forest fire activity from meteosat second generation data. Environ Model Software 25(12):1909–1914. doi:10.1016/j.envsoft.2010.06.003

    Article  Google Scholar 

  13. Chladil M, Nunez M (1995) Assessing grassland moisture and biomass in Tasmania – the application of remote-sensing and empirical-models for a cloudy environment. Int J Wildland Fire 5(3):165. doi:10.1071/WF9950165

    Article  Google Scholar 

  14. Finney M (1994) FARSITE: a fire area simulator for fire managers. In: The proceedings of the Biswell symposium, pp 55–56 . Retrieved from http://www.firemodels.org/downloads/farsite/publications/Finney_1995_PSW-GTR-158_pp55-56.pdf

  15. Morais ME (2001) Comparing spatially explicit models of fire spread through chaparral fuels: a new algorithm based upon the Rothermel fire spread equation. Retrieved from http://firecenter.berkeley.edu/hfire/hfire_fire_spread_body.pdf

  16. Tymstra C, Flannigan MD, Armitage OB, Logan K (2007) Impact of climate change on area burned in Alberta’s boreal forest. Int J Wildland Fire 16(2):153. doi:10.1071/WF06084

    Article  Google Scholar 

  17. Chi S, Lim Y, Lee J, Lee J (2003) A simulation-based decision support system for forest fire fighting. AI* IA 2003: advances in …, 2013. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-39853-0_40

  18. Minciardi R, Sacile R, Trasforini E (2009) Resource allocation in integrated preoperational and operational management of natural hazards. Risk Anal 29(1):62–75. doi:10.1111/j.1539-6924.2008.01154.x

    Article  Google Scholar 

  19. Donovan G, Rideout D (2003) An integer programming model to optimize resource allocation for wildfire containment. For Sci 49(2):331–335, Retrieved from http://www.ingentaconnect.com/content/saf/fs/2003/00000049/00000002/art00017

  20. Fried JS, Gilless JK, Spero J (2006) Analysing initial attack on wildland fires using stochastic simulation. Int J Wildland Fire 15(1):137. doi:10.1071/WF05027

    Article  Google Scholar 

  21. de la Asunción M, Castillo L, Fernámdez-Olivares J, García-Pérez O, González A, Palao F (2005) SIADEX: an interactive knowledge-based planner for decision support in forest fire fighting. AI Commun 18(4):257–268, Retrieved from http://dl.acm.org/citation.cfm?id=1218883.1218887

    MathSciNet  Google Scholar 

  22. Moura D, Oliveira E (2007) Fighting fire with agents – an agent coordination model for simulated firefighting. In: Proceedings of the 2007 spring simulation multiconference, vol 1, pp 71–78. Retrieved from http://dl.acm.org/citation.cfm?id=1404680.1404691

  23. Sarmento LM (2004) An emotion-based agent architecture. Retrieved from http://paginas.fe.up.pt/~niadr/PUBLICATIONS/thesis_Masters/LuisSarmento.pdf

  24. Yi S, Shi J (2009) An agent-based simulation model for occupant evacuation under fire conditions. In: 2009 WRI global congress on intelligent systems, IEEE, pp 27–31. doi:10.1109/GCIS.2009.442

  25. Ntaimo L (2004) Forest fire spread and suppression in DEVS. Simulation 80(10):479–500. doi:10.1177/0037549704050918

    Article  Google Scholar 

  26. Muzy A, Innocenti E, Aiello A, Santucci J-F, Wainer G (2002) Cell-DEVS quantization techniques in a fire spreading application. In: Proceedings of the winter simulation conference, vol 1. IEEE, pp 542–549. doi:10.1109/WSC.2002.1172929

  27. Wainer GA (2004) Modeling and simulation of complex systems with Cell-DEVS. In: Ingalls RG, Rossett MD, Smith JS, Peters BA (eds) Proceedings of the 2004 winter simulation conference

    Google Scholar 

  28. Yang J, Chen H, Hariri S, Parashar M (2005) Self-optimization of large scale wildfire simulations. Computational Science–ICCS …, pp 615–622. Retrieved from http://link.springer.com/chapter/10.1007/11428831_76

  29. ITU-T. (2011) Specification and description language – overview of SDL-2010, p 68

    Google Scholar 

  30. Fonseca i Casas P, Colls M, Casanovas J (2010) Towards a representation of environmental models using specification and description language-from the fibonacci model to a Wildfire Model. In: KEOD. Retrieved from http://upcommons.upc.edu/handle/2117/11032

  31. Gronewold A, Sonnenschein M (1998) Event-based modelling of ecological systems with asynchronous cellular automata. Ecological Modelling, 18. Retrieved from http://www.sciencedirect.com/science/article/pii/S0304380098000179

  32. Niazi Ma, Siddique Q, Hussain A, Kolberg M (2010) Verification & validation of an agent-based forest fire simulation model. In: Proceedings of the 2010 spring simulation multiconference on – SpringSim’10, 1. doi:10.1145/1878537.1878539

  33. Nader B, Filippi J, Bisgambiglia P (2011) An experimental frame for the simulation of forest fire spread. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu M (eds) Proceedings of the 2011 winter simulation conference, pp 1010–1022. Retrieved from http://dl.acm.org/citation.cfm?id=2431637

  34. Bratten FW (1978) Containment tables for initial attack on forest fires. Fire Technol 14(4):297–303. doi:10.1007/BF01998389

    Article  Google Scholar 

  35. Beaumont KP, Mackay DA, Whalen MA (2012) The effects of prescribed burning on epigaeic ant communities in eucalypt forest of South Australia. For Ecol Manage 271:147–157. doi:10.1016/j.foreco.2012.02.007

    Article  Google Scholar 

  36. Stephan K, Kavanagh KL, Koyama A (2012) Effects of spring prescribed burning and wildfires on watershed nitrogen dynamics of central Idaho headwater areas. For Ecol Manage 263:240–252. doi:10.1016/j.foreco.2011.09.013

    Article  Google Scholar 

  37. Rytwinski A, Crowe KA (2010) A simulation-optimization model for selecting the location of fuel-breaks to minimize expected losses from forest fires. For Ecol Manage 260(1):1–11. doi:10.1016/j.foreco.2010.03.013

    Article  Google Scholar 

  38. Fei Y, Xianlin Q, Bo H, Xi Z, Zengyuan L (2013) 1 . Automatic extraction of active fire line using Landsat imagery. In: Proceedings of dragon 2 final results and dragon 3 kick-off symposium, Noordwijk, p 649

    Google Scholar 

  39. Keen PGW (1980) Decision support systems: a research perspective. Cambridge, Massachusetts: Center for Information Systems Research, Afred P. Sloan School of Management. Retrieved from http://hdl.handle.net/1721.1/47172

  40. Haettenschwiler P (1999) Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. In: Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. vdf Hochschulverlag, Zurich, pp 189–208

    Google Scholar 

  41. Power DJ (1997) What is a DSS? DSstar, 21 October 1997, 1(3). http://dssresources.com/papers/whatisadss/index.html

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Acknowledgments

This study was funded by the Ministry of Science and Innovation, Spain. Project reference TIN2011-29494-C03-03.

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Authors and Affiliations

  1. Universitat Politècnica de Catalunya, Edifici B5-S102, C/Jordi Girona, 31, Barcelona, 08025, Spain

    Jaume Figueras Jové, Pau Fonseca i Casas, Antoni Guasch Petit & Josep Casanovas

Authors
  1. Jaume Figueras Jové
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  2. Pau Fonseca i Casas
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  3. Antoni Guasch Petit
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  4. Josep Casanovas
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Corresponding author

Correspondence to Antoni Guasch Petit .

Editor information

Editors and Affiliations

  1. Institute of Computer Science and ‘Gheorghe Asachi’ Technical University of Iasi, Iasi, Romania

    Horia-Nicolai Teodorescu

  2. Faculty of Industrial Engineering and Management, Samuel Neaman Institute for Advanced Studies,Technion – Israel Institute of Technology, Haifa, Israel

    Alan Kirschenbaum

  3. Institute of Mathematics and Computer Science of the Academy of Sciences of Moldova, Chisinau, Moldova

    Svetlana Cojocaru

  4. Department of Global Health and Populati, Harvard University, Cambridge, Massachusetts, USA

    Claude Bruderlein

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Figueras Jové, J., Fonseca i Casas, P., Guasch Petit, A., Casanovas, J. (2014). FireFight: A Decision Support System for Forest Fire Containment. In: Teodorescu, HN., Kirschenbaum, A., Cojocaru, S., Bruderlein, C. (eds) Improving Disaster Resilience and Mitigation - IT Means and Tools. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9136-6_19

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  • DOI: https://doi.org/10.1007/978-94-017-9136-6_19

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-9135-9

  • Online ISBN: 978-94-017-9136-6

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