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Wildland Fire Spread Modeling Using Convolutional Neural Networks

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Abstract

The computational cost of predicting wildland fire spread across large, diverse landscapes is significant using current models, which limits the ability to use simulations to develop mitigation strategies or perform forecasting. This paper presents a machine learning approach to estimate the time-resolved spatial evolution of a wildland fire front using a deep convolutional inverse graphics network (DCIGN). The DCIGN was trained and tested for wildland fire spread across simple homogeneous landscapes as well as heterogeneous landscapes having complex terrain. Data sets for training, validation, and testing were created using computational models. The model for homogeneous landscapes was based on a rate of spread from the model of Rothermel, while heterogeneous spread was modeled using FARSITE. Over 10,000 model predictions were made to determine burn maps in 6 h increments up to 24 h after ignition. Overall the predicted burn maps from the DCIGN-based approach agreed with simulation results, with mean precision, sensitivity, F-measure, and Chan–Vese similarity of 0.97, 0.92, 0.93, and 0.93, respectively. Noise in the input parameters was found to not significantly impact the DCIGN-based predictions. The computational cost of the method was found to be significantly better than the computational model for heterogeneous spatial conditions where a reduction in simulation time of \(10^{2}{-}10^{5}\) was observed. In addition, the DCIGN-based approach was shown to be capable of predicting burn maps further in the future by recursively using previous predictions as inputs to the DCIGN. The machine learning DCIGN approach was able to provide fire spread predictions at a computational cost three orders of magnitude less than current models.

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References

  1. Weber R (1991) Modelling fire spread through fuel beds, Prog Energy Combust Sci 17(1):67

    Article  Google Scholar 

  2. Sullivan A (2008) A review of wildland fire spread modelling, 1990-present 1: physical and quasi-physical models. arXiv:0706.3074v1 [physics.geo-ph]

  3. Sullivan A (2013) A review of wildland fire spread modelling, 1990-present 2: empirical and quasi-empirical models. arXiv:0706.4128 [physics.geo-ph]

  4. Simeoni A (2015) Wildland fires. In: Hurley MJ, Gottuk DT, Hall JR Jr, Harada K, Kuligowski ED, Puchovsky M, Watts JM Jr, Wieczorek CJ (eds) SFPE handbook of fire protection engineering. Springer, pp 3283–3302

  5. Rothermel RC et al (1972) A mathematical model for predicting fire spread in wildland fuels. Technical report, USDA Forest Service

  6. Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with rothermel’s surface fire spread model. Technical report, USDA Forest Service

  7. Finney MA (1999) Mechanistic modeling of landscape fire patterns, spatial modeling of forest landscapes: approaches and applications. Cambridge University Press, Cambridge, pp 186–209

    Google Scholar 

  8. Finney MA et al (1998) FARSITE, fire area simulator-model development and evaluation, vol 3. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden

    Book  Google Scholar 

  9. Rehm RG, McDermott RJ (2009) Fire-front propagation using the level set method. US Department of Commerce, National Institute of Standards and Technology, Gaithersburg

    Book  Google Scholar 

  10. Lautenberger C (2013) Wildland fire modeling with an eulerian level set method and automated calibration. Fire Saf J 62:289

    Article  Google Scholar 

  11. Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. Int J Wildland Fire 16(1):1

    Article  Google Scholar 

  12. Lattimer A, Borggaard J, Gugercin S, Luxbacher K, Lattimer B (2016) Computationally efficient wildland fire spread models. In: Proceedings of the 14th international fire science & engineering conference, pp 305–315

  13. Rochoux MC, Delmotte B, Cuenot B, Ricci S, Trouvé A (2013) Regional-scale simulations of wildland fire spread informed by real-time flame front observations. Proc Combust Inst 34(2):2641

    Article  Google Scholar 

  14. Rochoux MC, Ricci S, Lucor D, Cuenot B, Trouvé A (2014) Towards predictive data-driven simulations of wildfire spread—part i: reduced-cost ensemble Kalman filter based on a polynomial chaos surrogate model for parameter estimation. Nat Hazards Earth Syst Sci 14(11):2951

    Article  Google Scholar 

  15. Rochoux MC, Emery C, Ricci S, Cuenot B, Trouvé A (2015) Towards predictive data-driven simulations of wildfire spread—part ii: ensemble Kalman filter for the state estimation of a front-tracking simulator of wildfire spread. Nat Hazards Earth Syst Sci 15(8):1721

    Article  Google Scholar 

  16. Rios O, Pastor E, Valero M, Planas E (2016) Short-term fire front spread prediction using inverse modelling and airborne infrared images. Int J Wildland Fire 25(10):1033

    Article  Google Scholar 

  17. Zhang C, Rochoux M, Tang W, Gollner M, Filippi JB, Trouvé A (2017) Evaluation of a data-driven wildland fire spread forecast model with spatially-distributed parameter estimation in simulations of the fireflux i field-scale experiment. Fire Saf J 91:758

    Article  Google Scholar 

  18. Gu F, Hu X (2008) In 2008 winter simulation conference, pp 2852–2860. IEEE

  19. Xue H, Gu F, Hu X (2012) Data assimilation using sequential monte carlo methods in wildfire spread simulation. ACM Trans Model Comput Simul (TOMACS) 22(4):23

    Article  Google Scholar 

  20. Da Silva W, Rochoux M, Orlande H, Colaço M, Fudym O, El Hafi M, Cuenot B, Ricci S (2014) Application of particle filters to regional-scale wildfire spread. High Temp High Press 43:415

    Google Scholar 

  21. Bai F, Gu F, Hu X, Guo S (2016) Particle routing in distributed particle filters for large-scale spatial temporal systems. IEEE Trans Parallel Distrib Syst 27(2):481

    Article  Google Scholar 

  22. Mandel J, Beezley JD, Kochanski AK, Kondratenko VY, Kim M (2012) Assimilation of perimeter data and coupling with fuel moisture in a wildland fire—atmosphere dddas. Proc Comput Sci 9:1100

    Article  Google Scholar 

  23. Rochoux MC, Emery C, Ricci S, Cuenot B, Trouvé A (2014) Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position. Fire Saf Sci 11:1443

    Article  Google Scholar 

  24. Safi Y, Bouroumi A (2013) Prediction of forest fires using artificial neural networks, Appl Math Sci 7(6):271

    Google Scholar 

  25. Castelli M, Vanneschi L, Popovič A (2015) Predicting burned areas of forest fires: an artificial intelligence approach. Fire Ecol 11(1):106

    Article  Google Scholar 

  26. Storer J, Green R (2016) PSO trained neural networks for predicting forest fire size: a comparison of implementation and performance. In: 2016 international joint conference on neural networks (IJCNN), pp 676–683

  27. Naganathan H, Seshasayee SP, Kim J, Chong WK, Chou JS (2016) Wildfire predictions: determining reliable models using fused dataset. Glob J Comput Sci Technol 16(4):35–46

    Google Scholar 

  28. Cao Y, Wang M, Liu K (2017) Wildfire susceptibility assessment in southern china: a comparison of multiple methods. Int J Disaster Risk Sci 8(2):164

    Article  Google Scholar 

  29. McCormick RJ, Brandner TA, Allen TF (2001) Toward a theory of meso-scale wildfire modeling: a complex systems approach using artificial neural networks. Ph.D. thesis, University of Wisconsin, Madison

  30. McCormick RJ (2002) On developing a meso-theoretical viewpoint of complex systems by exploring the use of artificial neural networks in modeling wildfires. In: ForestSAT symposium, Edinburgh

  31. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541

    Article  Google Scholar 

  32. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25: 26th annual conference on neural information processing systems 2012, 3–6 December 2012. Lake Tahoe, NV, pp 1097–1105

  33. Goodfellow I, Bengio Y, Courville A (2016) Deep learning (MIT Press). http://www.deeplearningbook.org. Accessed 27 Nov 2018

  34. Kulkarni TD, Whitney WF, Kohli P, Tenenbaum J (2015) Deep convolutional inverse graphics network. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28: annual conference on neural information processing systems 2015, 7–12 December 2015. Montreal, QC, pp 2539–2547

  35. Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016) Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, 5–10 December 2016. Barcelona, pp 2172–2180

  36. Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, 5–10 December 2016. Barcelona, pp 469–477

  37. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

  38. Yi Z, Zhang H, Tan P, Gong M (2017) Dualgan: unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE international conference on computer vision, pp 2849–2857

  39. Albini FA (1976) Estimating wildfire behavior and effects. Technical report, USDA Forest Service

  40. Andrews PL (2012) Modeling wind adjustment factor and midflame wind speed for Rothermel’s surface fire spread model, General technical reports RMRS-GTR-266, vol 39. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, p 213

  41. Wagner CV (1969) A simple fire-growth model. For Chron 45(2):103

    Article  MathSciNet  Google Scholar 

  42. Green D, Gill AM, Noble I (1983) Fire shapes and the adequacy of fire-spread models. Ecol Model 20(1):33

    Article  Google Scholar 

  43. Andrews PL (2009) Behaveplus fire modeling system, version 5.0: variables. General technical reports RMRS-GTR-213 revised, vol 111. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, p 213

  44. Nelson RM Jr (2002) An effective wind speed for models of fire spread. Int J Wildland Fire 11(2):153

    Article  Google Scholar 

  45. Rollins MG (2009) Landfire: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18(3):235

    Article  Google Scholar 

  46. Maas Al, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. Proc ICML 30:3

    Google Scholar 

  47. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

  48. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Accessed 14 Apr 2018

  49. Filippi JB, Mallet V, Nader B (2014) Representation and evaluation of wildfire propagation simulations. Int J Wildland Fire 23(1):46

    Article  Google Scholar 

  50. Zhang C, Collin A, Moireau P, Trouvé A, Rochoux M (2019) Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: application to the rxcadre field-scale experiment. Proc Combust Inst 37(3):4201

    Article  Google Scholar 

  51. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266

    Article  MATH  Google Scholar 

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Correspondence to Jonathan L. Hodges.

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Hodges, J.L., Lattimer, B.Y. Wildland Fire Spread Modeling Using Convolutional Neural Networks. Fire Technol 55, 2115–2142 (2019). https://doi.org/10.1007/s10694-019-00846-4

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