975
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Modelling and optimisation of extinction actions for wildfire suppression

, , ORCID Icon, & ORCID Icon
Pages 3584-3595 | Received 14 Jun 2023, Accepted 14 Jun 2023, Published online: 11 Sep 2023

References

  • Alexandridis, A., L. Russo, D. Vakalis, G. V. Bafas, and C. I. Siettos. 2011. Wildland fire spread modelling using cellular automata: Evolution in large-scale spatially heterogeneous environments under fire suppression tactics. Int. J. Wildland Fire 20 (5):633–47. doi:10.1071/WF09119.
  • Alexandridis, A., D. Vakalis, C. Siettos, and G. Bafas. 2008. A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses island in 1990. Appl. Math. Comput. 204 (1):191–201. doi:10.1016/j.amc.2008.06.046.
  • Berjak, S. G., and J. W. Hearne. 2002. An improved cellular automaton model for simulating fire in a spatially heterogeneous savanna system. Ecol. Modell 148 (2):133–51. doi:10.1016/S0304-3800(01)00423-9.
  • Bertsimas, D., J. D. Griffith, V. Gupta, M. J. Kochenderfer, and V. V. Mišić. 2017. A comparison of monte carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems. Eur. J. Oper. Res. 263 (2):664–78. doi:10.1016/j.ejor.2017.05.032.
  • Chaslot, G., S. Bakkes, I. Szita, and P. Spronck. 2021. Monte-Carlo Tree Search: A new framework for game AI. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 4 (1):216–217. doi:10.1609/aiide.v4i1.18700.
  • Cheng, S., I. C. Prentice, Y. Huang, Y. Jin, Y.-K. Guo, and R. Arcucci. 2022. Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting. J. Comput. Phys. 464:111302. doi:10.1016/j.jcp.2022.111302.
  • Couëtoux, A., J.-B. Hoock, N. Sokolovska, O. Teytaud, and N. Bonnard. 2011. Continuous Upper Confidence Trees. In Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, ed. C. A. C. Coello, Vol. 6683. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-25566-3_32.
  • DellaSala, D. A., B. C. Baker, C. T. Hanson, L. Ruediger, and W. Baker. 2022. Have western USA fire suppression and megafire active management approaches become a contemporary Sisyphus? Biol. Conserv. 268:109499. doi:10.1016/j.biocon.2022.109499.
  • de Oliveira, P. M., M. P. Sitte, M. Zedda, A. Giusti, and E. Mastorakos. 2021. Low-order modeling of high-altitude relight of jet engine combustors. Int. J. Spray Combust. Dyn. 13 (1–2):20–34. doi:10.1177/17568277211021322.
  • Efstathiou, G., S. Gkantonas, A. Giusti, E. Mastorakos, C. M. Foale, and R. R. Foale. 2023. Simulation of the December 2021 Marshall fire with a hybrid stochastic Lagrangian-cellular automata model. Fire Saf. J. 138:103795. doi:10.1016/j.firesaf.2023.103795.
  • Eyerich, P., T. Keller, and M. Helmert. 2010. High-quality policies for the Canadian traveler’s problem. Proceedings of the AAAI Conference on Artificial Intelligence 24 (1):51–58. doi:10.1609/aaai.v24i1.7542 .
  • Grant, G., J. Brenton, and D. Drysdale. 2000. Fire suppression by water sprays. Prog. Energ. Combust. 26 (2):79–130. doi:10.1016/S0360-1285(99)00012-X.
  • Griffith, J. D., M. J. Kochenderfer, R. J. Moss, V. V. Mišić, V. Gupta, and D. Bertsimas. 2017. Automated dynamic resource allocation for wildfire suppression. Lincoln Lab J. 22 (2): 38–59.
  • Haghani, M., E. Kuligowski, A. Rajabifard, and C. A. Kolden. 2022. The state of wildfire and bushfire science: Temporal trends, research divisions and knowledge gaps. Saf. Sci. 153:105797. doi:10.1016/j.ssci.2022.105797.
  • Ingalsbee, T., and U. Raja. 2015. The rising costs of wildfire suppression and the case for ecological fire use. ‘The Ecol. Importance Of Mixed-Severity Fire’, Chapter. 12:348–71.
  • Karafyllidis, I., and A. Thanailakis. 1997. A model for predicting forest fire spreading using cellular automata. Ecol Modell 99 (1):87–97. doi:10.1016/S0304-3800(96)01942-4.
  • Mastorakos, E., S. Gkantonas, G. Efstathiou, and A. Giusti. 2023. A hybrid stochastic Lagrangian - cellular automata framework for modelling fire propagation in inhomogeneous terrains. Proc. Combust. Inst. 39 (3):3853–3862. doi:10.1016/j.proci.2022.07.240.
  • Minas J. P., J. W. Hearne, and D. L. Martell. 2014. A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts. Eur. J. Oper. Res. 232(2):412–422. doi:10.1016/j.ejor.2013.07.026.
  • National Wildfire Coordinating Group. 1996. Wildland Fire Suppression Tactics Reference Guide. Boise, ID, US: National Wildfire Coordinating Group.
  • Neophytou, A., E. Richardson, and E. Mastorakos. 2012. Spark ignition of turbulent recirculating non-premixed gas and spray flames: A model for predicting ignition probability. Combust. Flame 159 (4):1503–22. doi:10.1016/j.combustflame.2011.12.015.
  • Penney, G., D. Habibi, and M. Cattani. 2019. Firefighter tenability and its influence on wildfire suppression. Fire Saf. J. 106:38–51. doi:10.1016/j.firesaf.2019.03.012.
  • Russo, L., P. Russo, C. I. Siettos, and M. Hanewinkel. 2016. A complex network theory approach for the spatial distribution of fire breaks in heterogeneous forest landscapes for the control of wildland fires. PLoS One 11 (10):1–18. doi:10.1371/journal.pone.0163226.
  • Sullivan, A. L. 2017. Inside the inferno: Fundamental processes of wildland fire behaviour. Curr. For. Rep. 3 (2):132–49. doi:10.1007/s40725-017-0057-0.
  • Wahlqvist, J., E. Ronchi, S. M. Gwynne, M. Kinateder, G. Rein, H. Mitchell, N. Bénichou, C. Ma, A. Kimball, and E. Kuligowski. 2021. The simulation of wildland-urban interface fire evacuation: The wui-nity platform. Saf. Sci. 136:105145. doi:10.1016/j.ssci.2020.105145.
  • Xu, R., P. Yu, M. J. Abramson, F. H. Johnston, J. M. Samet, M. L. Bell, A. Haines, K. L. Ebi, S. Li, and Y. Guo. 2020. Wildfires, global climate change, and human health. N. Engl. J. Med. 383 (22):2173–81. doi:10.1056/NEJMsr2028985.
  • Zhou, S., and A. Erdogan. 2019. A spatial optimization model for resource allocation for wildfire suppression and resident evacuation. Comput. Ind. Eng. 138:106101. doi:10.1016/j.cie.2019.106101.