Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review

Abstract

The significant threat of wildfires to forest ecology and biodiversity, particularly in tropical and subtropical regions, underscores the necessity for advanced predictive models amidst shifting climate patterns. There is a need to evaluate and enhance wildfire prediction methods, focusing on their application during extended periods of intense heat and drought. This study reviews various wildfire modelling approaches, including traditional physical, semi-empirical, numerical, and emerging machine learning (ML)-based models. We critically assess these models’ capabilities in predicting fire susceptibility and post-ignition spread, highlighting their strengths and limitations. Our findings indicate that while traditional models provide foundational insights, they often fall short in dynamically estimating parameters and predicting ignition events. Cellular automata models, despite their potential, face challenges in data integration and computational demands. Conversely, ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets, though they encounter interpretability issues. This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths. By incorporating data assimilation techniques with dynamic forecasting models, the predictive capabilities of ML-based predictions can be significantly enhanced. This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications, ultimately contributing to more effective wildfire mitigation and management strategies. Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.

References

AndersonHE. Heat transfer and fire spread, 1969 Ogden Utah Intermountain Forest and Range Experiment Station

AndersonWR, CruzMG, FernandesPM, McCawL, VegaJA, BradstockRA, FogartyL, GouldJ, McCarthyG, Marsden-SmedleyJB, MatthewsS, MattingleyG, PearceHG, van WilgenBW. A generic empirical-based model for predicting rate of fire spread in shrublands. Int J Wildland Fire, 2015, 24(4): 443

ArtésT, CortésA, MargalefT. Large forest fire spread prediction: data and computational science. Procedia Comput Sci, 2016, 80: 909-918

BabushkaA, BabiyL, ChetverikovB, SevrukA. Research of forest fires using remote sensing data (on the example of the Chornobyl exclusion zone). Geodesy Cartogr Aer Photogr, 2021, 94: 35-43

Bamdale R, Shelar S, Khandekar V (2021) How to tackle climate change using artificial intelligence. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). Kharagpur, India. IEEE. pp 1–7.

BeiX, YaoY, ZhangL, LinY, LiuS, JiaK, ZhangX, ShangK, YangJ, ChenX, GuoX. Estimation of daily terrestrial latent heat flux with high spatial resolution from MODIS and Chinese GF-1 data. Sensors (Basel), 2020, 20(10): E2811

BorghesioL. Can fire avoid massive and rapid habitat change in Italian heathlands?. J Nat Conserv, 2014, 22(1): 68-74

BuiDT, LeHV, HoangND. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Ecol Inform, 2018, 48: 104-116

BurrowsN, WardB, RobinsonA. Fire behaviour in Spinifex fuels on the gibson desert nature reserve. West Aust J Arid Environ, 1991, 20(2): 189-204

Canadian department of forestry (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. (Vol. 3). Forestry Canada, Science and Sustainable Development Directorate.

CartaF, ZiddaC, PutzuM, LoruD, AneddaM, GiustoD. Advancements in forest fire prevention: a comprehensive survey. Sensors (Basel), 2023, 23(14): 6635

Castle D, Mell WE, Miller FJ (2013) Examination of the Wildland-Urban Interface Fire Dynamics simulator in modeling of laboratory-scale surface-to-crown fire transition. 8th Natl Combust Meet. 4: 3710–3722.

Catchpole WR, Bradstock R, Choate J, Fogarty L, Gellie N, McCarthy G, Mccaw L, Marsden-Smedle J, Pearce H (1998a) Cooperative development of equations for heathland fire behaviour. In: Viegas, D.X. (ed). Proceedings, 3rd International Conference on Forest Fire Research and 14th Fire and Forest Meteorology Conference, Luso, Coimbra, Portugal, 16–20 November, pp 631–645.

CatchpoleWR, CatchpoleEA, ButlerBW, RothermelRC, MorrisGA, LathamDJ. Rate of spread of free-burning fires in woody fuels in a wind tunnel. Combust Sci Technol, 1998, 131(1–6): 1-37

CekirgeHM. Propagation of fire fronts in forests. Comput Math Appl, 1978, 4(4): 325-332

ChaudharyLB, BajpaiO, BeheraSK, SahuN. A new species of Oxytropis (Fabaceae: Papilionoideae) from India. Phytotaxa., 2013, 155(1): 50-58

CheneyNP, GouldJS, CatchpoleWR. Prediction of fire spread in grasslands. Int J Wildland Fire, 1998, 8(1): 1

ClarkTL, CoenJ, LathamD. Description of a coupled atmosphere - fire model. Int J Wildland Fire, 2004, 13(1): 49

CoenJ. Some requirements for simulating wildland fire behavior using insight from coupled weather—wildland fire models. Fire, 2018, 1(1): 6

CoenJL. Simulation of the big Elk fire using coupled atmosphere-fire modeling. Int J Wildland Fire, 2005, 14(1): 49

CoenJL, CameronM, MichalakesJ, PattonEG, RigganPJ, YedinakKM. WRF-fire: coupled weather–wildland fire modeling with the weather research and forecasting model. J Appl Meteor Climatol, 2013, 52(1): 16-38

CoenJL, SchroederW, ConwayS, TarnayL. Computational modeling of extreme wildland fire events: a synthesis of scientific understanding with applications to forecasting land management and firefighter safety. J Comput Sci, 2020, 45

ColmanJJ, LinnRR. Separating combustion from pyrolysis in HIGRAD/FIRETEC. Int J Wildland Fire, 2007, 16(4): 493

DahlN, XueHD, HuXL, XueM. Coupled fire–atmosphere modeling of wildland fire spread using DEVS-FIRE and ARPS. Nat Hazards, 2015, 77(2): 1013-1035

De MestreNJ, CatchpoleEA, AndersonDH, RothermelRC. Uniform propagation of a planar fire front without wind. Combust Sci Technol, 1989, 65(4–6): 231-244

DenhamM, WendtK, BianchiniG, CortésA, MargalefT. Dynamic data-driven genetic algorithm for forest fire spread prediction. J Comput Sci, 2012, 3(5): 398-404

DoK, MahishM, YeganehAK, GaoZQ, BlanchardCL, IveyCE. Emerging investigator series: a machine learning approach to quantify the impact of meteorology on tropospheric ozone in the inland southern California. Environ Sci: Atmos, 2023, 3(8): 1159-1173

DoganA, BirantD. Machine learning and data mining in manufacturing. Expert Syst Appl, 2021, 166

EdenJM, KrikkenF, DrobyshevI. An empirical prediction approach for seasonal fire risk in the boreal forests. Int J Climatol, 2020, 40(5): 2732-2744

EskandariS. Application of a CA-based model to predict the fire front in Hyrcanian forests of Iran. Arab J Geosci, 2016, 9(17): 688

FAO. Global Forest Resources Assessment 2020 FAO. Rome, 2020

Fernandes P (1998) Fire spread modelling in Portuguese shrubland. Proc. 3rd Intern. Conf. Forest Fire Research & 14th Fire and Forest Meteorology Conf., Viegas, DX (Ed.), ADAI.

FilippiJB, BosseurF, MariC, LacC, Le MoigneP, CuenotB, VeynanteD, CariolleD, BalbiJH. Coupled atmosphere-wildland fire modelling. J Adv Model Earth Syst, 2009, 1(4): 1892

FilippiJB, BosseurF, PialatX, SantoniPA, StradaS. Mari C (2011) Simulation of coupled fire/atmosphere interaction with the MesoNH-ForeFire models. J Combust, 2011, 1

Finney MA (1998) FARSITE: Fire Area Simulator-model development and evaluation. https://doi.org/10.2737/rmrs-rp-4

FlanniganM, StocksB, TuretskyM, WottonM. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob Change Biol, 2009, 15(3): 549-560

FlanniganMD, AmiroBD, LoganKA, StocksBJ, WottonBM. Forest fires and climate change in the 21st century. Mitig Adapt Strateg Glob Change, 2006, 11(4): 847-859

FonsWL. Analysis of fire spread in light forest fuels. J Agric Res, 1946, 72: 93-121

FrandsenWH. Fire spread through porous fuels from the conservation of energy. Combust Flame, 1971, 16(1): 9-16

FrangiehN, AccaryG, MorvanD, MéradjiS, BessonovO. Wildfires front dynamics: 3D structures and intensity at small and large scales. Combust Flame, 2020, 211: 54-67

Ganapathi SubramanianS, CrowleyM. Using spatial reinforcement learning to build forest wildfire dynamics models from satellite images. Front ICT, 2018, 5: 6

GengDT, YangG, NingJB, LiA, LiZG, MaSJ, WangXY, YuHZ. Modification of the Rothermel model parameters–the rate of surface fire spread of Pinus koraiensis needles under no-wind and various slope conditions. Int J Wildland Fire, 2024, 33(4): WF23118

GiannarosTM, KotroniV, LagouvardosK. IRIS-Rapid response fire spread forecasting system: Development calibration and evaluation. Agric for Meteor, 2019, 279

Global Forest Watch (2014) World Resources Institute, Global Forest Watch, [WWW Document] Retrieved from https://www.globalforestwatch.org/dashboards/global/

GonzálezTM, González-TrujilloJD, MuñozA, ArmenterasD. Effects of fire history on animal communities: a systematic review. Ecol Process, 2022, 11(1): 11

Grau-AndrésR, GrayA, DaviesGM, ScottEM, WaldronS. Burning increases post-fire carbon emissions in a heathland and a raised bog, but experimental manipulation of fire severity has no effect. J Environ Manage, 2019, 233: 321-328

Griffin GF, Allan GE (1984) Fire behaviour. Anticipating the inevitable: a patch burn strategy for fire management at Uluru (Ayers Rock-Mt Olga) National Park. Edited by Saxon, E. CSIRO Australia, Melbourne. pp 55–68.

GuoFT, ZhangLJ, JinS, TigabuM, SuZW, WangWH. Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests, 2016, 7(11): 250

HadisuwitoAS, HassanFH. A comparative study of drought factors in the Mcarthur forest fire danger index in Indonesian forest. Ecol Environ Conserv, 2021, 5: 202-206

HalofskyJE, PetersonDL, HarveyBJ. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest USA. Fire Ecol, 2020, 16(1): 4

HansonJE. MeyersRA. Cellular automata emergent phenomena in. Encyclopedia of complexity and systems science, 2009 New York Springer

HargroveWW, GardnerRH, TurnerMG, RommeWH, DespainDG. Simulating fire patterns in heterogeneous landscapes. Ecol Model, 2000, 135(2–3): 243-263

HoffmanCM, CanfieldJ, LinnRR, MellW, SiegCH, PimontF, ZieglerJ. Evaluating crown fire rate of spread predictions from physics-based models. Fire Technol, 2016, 52(1): 221-237

HongHY, TsangaratosP, IliaI, LiuJZ, ZhuAX, XuC. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County. China Sci Total Environ, 2018, 630: 1044-1056

HuotF, HuRL, GoyalN, SankarT, IhmeM, ChenYF. Next day wildfire spread: a machine learning dataset to predict wildfire spreading from remote-sensing data. IEEE Trans Geosci Remote Sens, 2022, 60: 1-13

JiangL, IslamS, CarlsonTN. Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data. Can J Remote Sens, 2004, 30(5): 769-787

JollyWM, CochraneMA, FreebornPH, HoldenZA, BrownTJ, WilliamsonGJ, BowmanDMJS. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun, 2015, 6: 7537

JosephsonAJ, CastañoD, KooE, LinnRR. Zonal-based emission source term model for predicting particulate emission factors in wildfire simulations. Fire Technol, 2021, 57(2): 943-971

Kansal A, Singh Y, Kumar N, Mohindru V (2015) Detection of forest fires using machine learning technique: a perspective. In: 2015 Third International Conference on Image Information Processing (ICIIP). Waknaghat, India. IEEE. pp 241–245.

KanwalR, RafaqatW, IqbalM, SongWG. Data-driven approaches for wildfire mapping and prediction assessment using a convolutional neural network (CNN). Remote Sens, 2023, 15(21): 5099

KarafyllidisI. Design of a dedicated parallel processor for the prediction of forest fire spreading using cellular automata and genetic algorithms. Eng Appl Artif Intell, 2004, 17(1): 19-36

KarafyllidisI, ThanailakisA. A model for predicting forest fire spreading using cellular automata. Ecol Model, 1997, 99(1): 87-97

KhalafMWA, JouibarySS, JahdiR. Performance analysis of ConvLSTM, FlamMap and CA algorithms to predict wildfire spread in golestan National Park. NE Iran Environ Model Assess, 2024, 29(3): 489-502

KhanmohammadiS, ArashpourM, GolafshaniEM, CruzMG, RajabifardA, BaiY. Prediction of wildfire rate of spread in grasslands using machine learning methods. Environ Model Softw, 2022, 156

KnorrW, JiangL, ArnethA. Climate CO₂ and human population impacts on global wildfire emissions. Biogeosciences, 2016, 13(1): 267-282

KochanskiAK, JenkinsMA, MandelJ, BeezleyJD, ClementsCB, KruegerS. Evaluation of WRF-SFIRE performance with field observations from the FireFlux experiment. Geosci Model Dev, 2013, 6(4): 1109-1126

KohJ. Gradient boosting with extreme-value theory for wildfire prediction. Extremes, 2023, 26(2): 273-299

KonevEV, SukhininAI. The analysis of flame spread through forest fuel. Combust Flame, 1977, 28: 217-223

LiXD, ZhangMX, ZhangSY, LiuJQ, SunSF, HuTX, SunL. Simulating forest fire spread with cellular automation driven by a LSTM based speed model. Fire, 2022, 5(1): 13

LinnR, ReisnerJ, ColmanJJ, WinterkampJ. Studying wildfire behavior using FIRETEC. Int J Wildland Fire, 2002, 11(4): 233

Linn RR (1997) A transport model for prediction of wildfire behavior. PhD Dissertation, the Dept. of Mechanical Engineering, New Mexico State Univ., Las Cruces, NM (US). https://doi.org/10.2172/505313

LinnRR, CunninghamP. Numerical simulations of grass fires using a coupled atmosphere–fire model: Basic fire behavior and dependence on wind speed. J Geophys Res, 2005, 110(D13): e2004jd005597

LinnRR, SiegCH, HoffmanCM, WinterkampJL, McMillinJD. Modeling wind fields and fire propagation following bark beetle outbreaks in spatially-heterogeneous pinyon-juniper woodland fuel complexes. Agric for Meteor, 2013, 173: 139-153

LourençoM, OliveiraLB, OliveiraJP, MoraA, OliveiraH, SantosR. An integrated decision support system for improving wildfire suppression management. ISPRS Int J Geo Inf, 2021, 10(8): 497

MandelJ, AmramS, BeezleyJD, KelmanG, KochanskiAK, KondratenkoVY, LynnBH, RegevB, VejmelkaM. Recent advances and applications of WRF–SFIRE. Nat Hazards Earth Syst Sci, 2014, 14(10): 2829-2845

MandelJ, BeezleyJD, KochanskiAK. Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011. Geosci Model Dev, 2011, 4(3): 591-610

MarjaniM, Ali AhmadiS, MahdianpariM. FirePred: a hybrid multi-temporal convolutional neural network model for wildfire spread prediction. Ecol Inform, 2023, 78

MarjaniM, MahdianpariM, MohammadimaneshF. CNN-BiLSTM: a novel deep learning model for near-real-time daily wildfire spread prediction. Remote Sens, 2024, 16(8): 1467

Marsden-SmedleyJB, CatchpoleWR. Fire behaviour modelling in Tasmanian buttongrass Moorlands. I. fuel characteristics. Int J Wildland Fire, 1995, 5(4): 203

MarshallG, ThompsonD, AndersonK, SimpsonB, LinnR, SchroederD. The impact of fuel treatments on wildfire behavior in North American boreal fuels: a simulation study using FIRETEC. Fire, 2020, 3(2): 18

McAlpineRS, WakimotoRH. The acceleration of fire from point source to equilibrium spread. For Sci, 1991, 37(5): 1314-1337

McArthur AG (1966) Weather and grassland fire behaviour. Forestry and Timber Bureau, (eds) Department of National Develop-ment), Leaflet 100. pp. 23, Canberra, Australia.

McArthur AG (1967) Fire behaviour in eucalypt forests. Australian Forestry and Timber Bureau, Leaflet No. 107, Canberra.

MellW, JenkinsMA, GouldJ, CheneyP. A physics-based approach to modelling grassland fires. Int J Wildland Fire, 2007, 16(1): 1

Mell WE, McDermott RJ, Forney GP (2010) Wildland fire behavior modeling: perspectives, new approaches and applications. In: Proceedings of 3rd Fire Behaviour and Fuels Conference, Spokane, Washington, USA. pp 45–62.

MendesJ, SouzaF, AraújoR, GonçalvesN. Genetic fuzzy system for data-driven soft sensors design. Appl Soft Comput, 2012, 12(10): 3237-3245

Mohammadian BisheE, AfshinH, FarhaniehB. Modified quasi-physical grassland fire spread model: sensitivity analysis. Sustainability, 2023, 15(18): 13639

MöldersN. Suitability of the weather research and forecasting (WRF) model to predict the June 2005 fire weather for interior Alaska. Weather Forecast, 2008, 23(5): 953-973

MoodyMJ, StollR, BaileyBN. Adaptation of QES-Fire a dynamically coupled fast response wildfire model for heterogeneous environments. Int J Wildland Fire, 2023, 32(5): 749-766

MoritzMA, BatlloriE, BradstockRA, GillAM, HandmerJ, HessburgPF, LeonardJ, McCaffreyS, OdionDC, SchoennagelT, SyphardAD. Learning to coexist with wildfire. Nature, 2014, 515: 58-66

NaserMZ. Mechanistically informed machine learning and artificial intelligence in fire engineering and sciences. Fire Technol, 2021, 57(6): 2741-2784

Neumann JV (1951) The general and logical theory of automata. in: Cerebral Mechanisms in Behavior; the Hixon Symposium. Wiley, Oxford, England pp 1–41.

NeumannJV. Theory of Self-reproducing automata, 1966 Urbana University of Illinois Press

NobelA, LizinS, WittersN, RineauF, MalinaR. The impact of wildfires on the recreational value of heathland: a discrete factor approach with adjustment for on-site sampling. J Environ Econ Manag, 2020, 101

NobleIR, GillAM, BaryGAV. McArthur’s fire-danger meters expressed as equations. Aust J Ecol, 1980, 5(2): 201-203

NorooziF, GhanbarianG, SafaeianR, PourghasemiHR. Forest fire mapping: a comparison between GIS-based random forest and Bayesian models. Nat Hazards, 2024, 120(7): 6569-6592

NurAS, KimYJ, LeeCW. Creation of wildfire susceptibility maps in plumas national forest using InSAR coherence, deep learning, and metaheuristic optimization approaches. Remote Sens, 2022, 14(17): 4416

O’ConnorCD, CalkinDE, ThompsonMP. An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. Int J Wildland Fire, 2017, 26(7): 587

OrD, Furtak-ColeE, BerliM, ShillitoR, EbrahimianH, Vahdat-AboueshaghH, McKennaSA. Review of wildfire modeling considering effects on land surfaces. Earth Sci Rev, 2023, 245

PagniPJ, PetersonTG. Flame spread through porous fuels. Symp Int Combust, 1973, 14(1): 1099-1107

PaisC, CarrascoJ, MartellDL, WeintraubA, WoodruffDL. Cell 2Fire: a cell-based forest fire growth model to support strategic landscape management planning. Front for Glob Change, 2021, 4

PastorE, ZárateL, PlanasE, ArnaldosJ. Mathematical models and calculation systems for the study of wildland fire behaviour. Prog Energy Combust Sci, 2003, 29(2): 139-153

Perumal R, van Zyl TL (2020) Comparison of recurrent neural network architectures for wildfire spread modelling. In: 2020 International SAUPEC/RobMech/PRASA Conference. Cape Town, South Africa. IEEE. pp 1–6.

PimontF, DupuyJL, LinnRR, DupontS. Validation of FIRETEC wind-flows over a canopy and a fuel-break. Int J Wildland Fire, 2009, 18(7): 775

PlucinskiMP, SullivanAL, McCawWL. Comparing the performance of daily forest fire danger summary metrics for estimating fire activity in southern Australian forests. Int J Wildland Fire, 2020, 29(10): 926

QayyumF, Abdel SameeN, AlabdulhafithM, AzizA, HijjawiM. Retraction note: shapley-based interpretation of deep learning models for wildfire spread rate prediction. Fire Ecol, 2024, 20(1): 69

Radke D, Hessler A, Ellsworth D (2019) FireCast: leveraging deep learning to predict wildfire spread. In: IJCAI. pp 4575–4581.

ReidJS, HyerEJ, JohnsonRS, HolbenBN, YokelsonRJ, ZhangJL, CampbellJR, ChristopherSA, Di GirolamoL, GiglioL, HolzRE, KearneyC, MiettinenJ, ReidEA, TurkFJ, WangJ, XianP, ZhaoGY, BalasubramanianR, ChewBN, JanjaiS, LagrosasN, LestariP, LinNH, MahmudM, NguyenAX, NorrisB, OanhNTK, OoM, SalinasSV, WeltonEJ, LiewSC. Observing and understanding the Southeast Asian aerosol system by remote sensing: an initial review and analysis for the seven Southeast Asian studies (7SEAS) program. Atmos Res, 2013, 122: 403-468

ReisnerJ, WynneS, MargolinL, LinnR. Coupled atmospheric–fire modeling employing the method of averages. Mon Wea Rev, 2000, 128(10): 3683-3691

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels; INT-115; U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT, USA. (accessed on 16 September 2019) Available online: https://www.fs.usda.gov/treesearch/pubs/32533

RuiXP, HuiS, YuXT, ZhangGY, WuB. Forest fire spread simulation algorithm based on cellular automata. Nat Hazards, 2018, 91(1): 309-319

SachdevaS, BhatiaT, VermaAK. GIS-based evolutionary optimized gradient boosted decision trees for forest fire susceptibility mapping. Nat Hazards, 2018, 92(3): 1399-1418

SanabriaLA, QinX, LiJ, CechetRP, LucasC. Spatial interpolation of McArthur’s forest fire danger index across Australia: observational study. Environ Model Softw, 2013, 50: 37-50

SantoniPA, BalbiJH. Modelling of two-dimensional flame spread across a sloping fuel bed. Fire Saf J, 1998, 31(3): 201-225

SayadYO, MousannifH, Al MoatassimeH. Predictive modeling of wildfires: a new dataset and machine learning approach. Fire Saf J, 2019, 104: 130-146

Scarth P, Armston J, Flood N, Denham R, Collett L, Watson F, Trevithick B, Muir J, Goodwin N, Tindall D, Phinn S (2015) Operational application of the landsat timeseries to address large area landcover understanding. Int Arch Photogramm Remote Sens Spatial Inf Sci XL-3/W3: 571–575. https://doi.org/10.5194/isprsarchives-xl-3-w3-571-2015

ShadrinD, IllarionovaS, GubanovF, EvteevaK, MironenkoM, LevchunetsI, BelousovR, BurnaevE. Wildfire spreading prediction using multimodal data and deep neural network approach. Sci Rep, 2024, 14(1): 2606

ShamsaeiK, JulianoTW, RobertsM, EbrahimianH, KosovicB, LareauNP, TacirogluE. Coupled fire-atmosphere simulation of the 2018 camp fire using WRF-fire. Int J Wildland Fire, 2023, 32(2): 195-221

SharplesJJ, McRaeRHD, WeberRO, GillAM. A simple index for assessing fire danger rating. Environ Model Softw, 2009, 24(6): 764-774

SimpsonCC, SharplesJJ, EvansJP. Resolving vorticity-driven lateral fire spread using the WRF-Fire coupled atmosphere–fire numerical model. Nat Hazards Earth Syst Sci, 2014, 14(9): 2359-2371

SinghS, SinghH, SharmaV, ShrivastavaV, KumarP, KangaS, SahuN, MerajG, FarooqM, SinghSK. Impact of forest fires on air quality in Wolgan Valley, New South Wales Australia—a mapping and monitoring study using google earth engine. Forests, 2022, 13(1): 4

SinghV, GuN. Towards an integrated generative design framework. Des Stud, 2012, 33(2): 185-207

Sneeuwjagt RJ, Peet GB (1985) Forest fire behaviour tables for Western Australia. Perth (Australia): Department of Conservation and Land Management.

SrivasT, ArtésT, de CallafonRA, AltintasI. Wildfire spread prediction and assimilation for FARSITE using ensemble Kalman filtering 1. Procedia Comput Sci, 2016, 80: 897-908

StephensonAG, ShabyBA, ReichBJ, SullivanAL. Estimating spatially varying severity thresholds of a forest fire danger rating system using max-stable extreme-event modeling. J Appl Meteor Climatol, 2015, 54(2): 395-407

StottP. How climate change affects extreme weather events. Science, 2016, 352(6293): 1517-1518

SullivanAL. Wildland surface fire spread modelling 1990–2007.1: physical and quasi-physical models. Int J Wildland Fire, 2009, 18(4): 349

SullivanAL. Wildland surface fire spread modelling 1990–2007.2: empirical and quasi-empirical models. Int J Wildland Fire, 2009, 18(4): 369

SunarF, ÖzkanC. Forest fire analysis with remote sensing data. Int J Remote Sens, 2001, 22(12): 2265-2277

TanML, PrasannaR, StockK, Hudson-DoyleE, LeonardG, JohnstonD. Mobile applications in crisis informatics literature: a systematic review. Int J Disaster Risk Reduct, 2017, 24: 297-311

TanskanenH, GranströmA, LarjavaaraM, PuttonenP. Experimental fire behaviour in managed Pinus sylvestris and Picea abies stands of Finland. Int J Wildland Fire, 2007, 16(4): 414

Telisin HP (1974) Flame radiation as a mechanism of fire spread in forests. Heat transfer in flames.

TerreiL, LamorletteA, GanteaumeA. Modelling the fire propagation from the fuel bed to the lower canopy of ornamental species used in wildland–urban interfaces. Int J Wildland Fire, 2019, 28(2): 113

The State of the World’s Forests (2020) FAO and UNEP. https://doi.org/10.4060/ca8642en

ThomasCM, SharplesJJ, EvansJP. Modelling the dynamic behaviour of junction fires with a coupled atmosphere–fire model. Int J Wildland Fire, 2017, 26(4): 331

ThomasPH. Some aspects of the growth and spread of fire in the open. Forestry, 1967, 40(2): 139-164

TianJF, ZhuCQ, JiangR, TreiberM. Review of the cellular automata models for reproducing synchronized traffic flow. Transp A Transp Sci, 2021, 17(4): 766-800

ValeroMM, JofreL, TorresR. Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis. Environ Model Softw, 2021, 141

van LieropP, LindquistE, SathyapalaS, FranceschiniG. Global forest area disturbance from fire insect pests diseases and severe weather events. For Ecol Manag, 2015, 352: 78-88

Van Wagner CE (1967) Calculations on forest fire spread by flame radiation (No. 1185). ottawa: queen’s printer.

Van WilgenBW, Le MaitreDC, KrugerFJ. Fire behaviour in South African fynbos (macchia) vegetation and predictions from rothermel’s fire model. J Appl Ecol, 1985, 22(1): 207

Venäläinen A, Heikinheimo M (2003) The Finnish Forest Fire Index Calculation System. In: Early Warning Systems for Natural Disaster Reduction. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 645–647.https://doi.org/10.1007/978-3-642-55903-7_88

WeberRO. Analytical models for fire spread due to radiation. Combust Flame, 1989, 78(3–4): 398-408

WeiseDR, BigingGS. A qualitative comparison of fire spread models incorporating wind and slope effects. For Sci, 1997, 43(2): 170-180

YaoJY, RaffuseSM, BrauerM, WilliamsonGJ, BowmanDMJS, JohnstonFH, HendersonSB. Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite. Remote Sens Environ, 2018, 206: 98-106

Yin H, Jin H, Zhao Y, Fan YG, Qin LW, Chen QH, Huang LY, Jia X, Liu LJ, Dai YH, Xiao Y (2018) The simulation of surface fire spread based on Rothermel model in windthrow area of Changbai Mountain (Jilin, China). In: Advances in Energy Science and Environment Engineering II, Zhuhai, China. In: AIP Conf. Proc. 1944, 020021–1–020021–7. https://doi.org/10.1063/1.5029735

ZacharakisI, TsihrintzisVA. Integrated wildfire danger models and factors: a review. Sci Total Environ, 2023, 899

ZaidiA. Predicting wildfires in Algerian forests using machine learning models. Heliyon, 2023, 9(7)

Zaker EsteghamatiM, GernayT, BanerjiS. Evaluating fire resistance of timber columns using explainable machine learning models. Eng Struct, 2023, 296

ZhaiCJ, ZhangSY, CaoZL, WangXM. Learning-based prediction of wildfire spread with real-time rate of spread measurement. Combust Flame, 2020, 215: 333-341

ZhangH, LiuH, MaGQ, ZhangY, YaoJX, GuC. A wildfire occurrence risk model based on a back-propagation neural network-optimized genetic algorithm. Front Energy Res, 2023, 10: 1031762

ZhangSY, LiuJQ, GaoHW, ChenXD, LiXD, HuaJ. Study on forest fire spread model of multi-dimensional cellular automata based on rothermel speed formula. Cerne, 2021, 27: e-102932

ZhangYL, TianLL. Examining and reforming the rothermel surface fire spread model under no-wind and zero-slope conditions for the Karst ecosystems. Forests, 2023, 14(6): 1088

ZhengZ, GaoYH, ZhangJ, ChenZJ. Modeling the susceptibility of forest fires using a genetic algorithm: a case study in mountain areas of southWestern China. Sci Program, 2022, 2022: 5502209

ZhengZ, HuangW, LiSN, ZengYN. Forest fire spread simulating model using cellular automaton with extreme learning machine. Ecol Model, 2017, 348: 33-43

Funding

University of the Sunshine Coast