Building Rothermel fire behaviour fuel models by genetic algorithm optimisation
Davide Ascoli A B , Giorgio Vacchiano A , Renzo Motta A and Giovanni Bovio A
+ Author Affiliations
- Author Affiliations
A Department of Agricultural, Forest and Food Sciences, University of Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Torino, Italy.
B Corresponding author. Email: d.ascoli@unito.it
International Journal of Wildland Fire 24(3) 317-328 https://doi.org/10.1071/WF14097
Submitted: 2 June 2014 Accepted: 16 November 2014 Published: 13 April 2015
Abstract
A method to build and calibrate custom fuel models was developed by linking genetic algorithms (GA) to the Rothermel fire spread model. GA randomly generates solutions of fuel model parameters to form an initial population. Solutions are validated against observations of fire rate of spread via a goodness-of-fit metric. The population is selected for its best members, crossed over and mutated within a range of model parameter values, until a satisfactory fitness is reached. We showed that GA improved the performance of the Rothermel model in three published custom fuel models for litter, grass and shrub fuels (root mean square error decreased by 39, 19 and 26%). We applied GA to calibrate a mixed grass–shrub fuel model, using fuel and fire behaviour data from fire experiments in dry heathlands of Southern Europe. The new model had significantly lower prediction error against a validation dataset than either standard or custom fuel models built using average values of inventoried fuels, and predictions of the Fuel Characteristics Classification System. GA proved a useful tool to calibrate fuel models and improve Rothermel model predictions. GA allows exploration of a continuous space of fuel parameters, making fuel model calibration computational effective and easily reproducible, and does not require fuel sampling. We suggest GA as a viable method to calibrate custom fuel models in fire modelling systems based on the Rothermel model.
Additional keywords: Fuel Characteristics Classification System, prescribed burning, Rothermel package for R, wildfire.
References
Albini FA (1976) Computer-based models of wildland fire behavior: a user’s manual. USDA Forest Service, Intermountain Forest and Range Experiment Station. (Ogden, UT)Alexander ME, Cruz MG (2012) Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height. International Journal of Wildland Fire 21, 95–113.
| Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height.Crossref | GoogleScholarGoogle Scholar |
Anderson HE (1969) Heat transfer and fire spread. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-69. (Ogden, UT)
Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-122. (Ogden, UT)
Andrews PL (2014) Current status and future needs of the BehavePlus Fire Modeling System. International Journal of Wildland Fire 23, 21–33.
| Current status and future needs of the BehavePlus Fire Modeling System.Crossref | GoogleScholarGoogle Scholar |
Andrews PL, Cruz MG, Rothermel RC (2013) Examination of the wind speed limit function in the Rothermel surface fire spread model. International Journal of Wildland Fire 22, 959–969.
| Examination of the wind speed limit function in the Rothermel surface fire spread model.Crossref | GoogleScholarGoogle Scholar |
Ascoli D, Bovio G (2010) Tree encroachment dynamics in heathlands of north-west Italy: the fire regime hypothesis. iForest 3, 137–143.
| Tree encroachment dynamics in heathlands of north-west Italy: the fire regime hypothesis.Crossref | GoogleScholarGoogle Scholar |
Ascoli D, Bovio G (2013) Prescribed burning in Italy: a review of issues, advances and challenges. iForest 6, 79–89.
| Prescribed burning in Italy: a review of issues, advances and challenges.Crossref | GoogleScholarGoogle Scholar |
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. Forest Ecology and Management 289, 69–77.
| Prescribed burning and browsing to control tree encroachment in southern European heathlands.Crossref | GoogleScholarGoogle Scholar |
Burgan RE (1987) Concepts and interpreted examples in advanced fuel modeling. USDA Forest Service, Intermountain Research Station, General Technical Report INT-238. (Ogden, UT)
Burgan RE, Rothermel RC (1984) BEHAVE: fire behavior prediction and fuel modeling system – FUEL subsystem. USDA Forest Service, Intermountain Forest and Range Experiment Station, Technical Report PMS 439–1. (Ogden, UT)
Byram GM (1959) Combustion of forest fuels. In ‘Forest Fire: Control and Use.’ (Ed. KP Davis) pp. 61–89. (McGraw-Hill: New York)
Cai L, He HS, Wu Z, Lewis BL, Liang Y (2014) Development of standard fuel models in boreal forests of northeast China through calibration and validation. PLoS ONE 9, e94043
| Development of standard fuel models in boreal forests of northeast China through calibration and validation.Crossref | GoogleScholarGoogle Scholar | 24714164PubMed |
Catchpole EA, Catchpole WR, Rothermel RC (1993) Fire behavior experiments in mixed fuel complexes. International Journal of Wildland Fire 3, 45–57.
| Fire behavior experiments in mixed fuel complexes.Crossref | GoogleScholarGoogle Scholar |
Catchpole WR, Catchpole EA, Butler BW, Rothermel RC, Morris GA, Latham DJ (1998) Rate of spread of free-burning fires in woody fuels in a wind tunnel. Combustion Science and Technology 131, 1–37.
| Rate of spread of free-burning fires in woody fuels in a wind tunnel.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXjs1Ggsbo%3D&md5=8e70078beac5a0ddef15f41e72bfe437CAS |
Chen TS, Tang K, Chen GL, Yao X (2012) A large population size can be unhelpful in evolutionary algorithms. Theoretical Computer Science 436, 54–70.
| A large population size can be unhelpful in evolutionary algorithms.Crossref | GoogleScholarGoogle Scholar |
Cheney NP (1990) Quantifying bushfires. Mathematical and Computer Modelling 13, 9–15.
| Quantifying bushfires.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Alexander ME (2010) Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies. International Journal of Wildland Fire 19, 377–398.
| Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Alexander ME (2013) Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software 47, 16–28.
| Uncertainty associated with model predictions of surface and crown fire rates of spread.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Fernandes PM (2008) Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands. International Journal of Wildland Fire 17, 194–204.
| Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands.Crossref | GoogleScholarGoogle Scholar |
Davies GM (2006) Fire behaviour and impact on heather moorlands. PhD Thesis, University of Edinburgh, Scotland.
Davies GM, Legg CJ (2011) Fuel moisture thresholds in the flammability of Calluna vulgaris. Fire Technology 47, 421–436.
| Fuel moisture thresholds in the flammability of Calluna vulgaris.Crossref | GoogleScholarGoogle Scholar |
Davies GM, Legg CJ, Smith AA, MacDonald AJ (2009) Rate of spread of fires in Calluna vulgaris-dominated Moorlands. Journal of Applied Ecology 46, 1054–1063.
| Rate of spread of fires in Calluna vulgaris-dominated Moorlands.Crossref | GoogleScholarGoogle Scholar |
Davies GM, Legg CJ, O’Hara R, MacDonald AJ, Smith AA (2010) Winter desiccation and rapid changes in the live fuel moisture content of Calluna vulgaris. Plant Ecology & Diversity 3, 289–299.
| Winter desiccation and rapid changes in the live fuel moisture content of Calluna vulgaris.Crossref | GoogleScholarGoogle Scholar |
Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141.
| Parameter control in evolutionary algorithms.Crossref | GoogleScholarGoogle Scholar |
Fernandes PM, Rego FC (1998) A new method to estimate fuel surface area-to-volume ratio using water immersion. International Journal of Wildland Fire 8, 59–66.
| A new method to estimate fuel surface area-to-volume ratio using water immersion.Crossref | GoogleScholarGoogle Scholar |
Fernandes PM, Catchpole WR, Rego FC (2000) Shrubland fire behaviour modelling with microplot data. Canadian Journal of Forest Research 30, 889–899.
| Shrubland fire behaviour modelling with microplot data.Crossref | GoogleScholarGoogle Scholar |
Fernandes PM, Davies MD, Ascoli D, Fernández C, Moreira F, Rigolot E, Stoof CR, Vega JA, Molina D (2013) Prescribed burning in southern Europe: developing fire management in a dynamic landscape. Frontiers in Ecology and the Environment 11, e4–e14.
| Prescribed burning in southern Europe: developing fire management in a dynamic landscape.Crossref | GoogleScholarGoogle Scholar |
Ferragut L, Monedero S, Asensio M, Ramirez J (2008) Scientific advances in fire modelling and its integration in a forest fire decision system. WIT Transactions on Ecology and the Environment 119, 31–38.
| Scientific advances in fire modelling and its integration in a forest fire decision system.Crossref | GoogleScholarGoogle Scholar |
Finney MA (2004) Landscape fire simulation and fuel treatment optimization. In ‘Methods for Integrating Modeling of Landscape Change: Interior Northwest Landscape Analysis System.’ (Tech. Eds JL Hayes, AA Ager, JR Barbour). USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-610, pp. 117–131. (Portland, OR)
Finney MA, Grenfell IC, McHugh CW, Seli RC, Trethewey D, Stratton RD, Brittain S (2011) A method for ensemble wildland fire simulation. Environmental Modeling and Assessment 16, 153–167.
| A method for ensemble wildland fire simulation.Crossref | GoogleScholarGoogle Scholar |
Frandsen WH (1973) Using the effective heating number as a weighting factor in Rothermel’s fire spread model. USDA Forest Service, Intermountain Forest and Range Experiment Station, Technical Report INT-10. (Ogden, UT)
Goldberg DE (1989) ‘Genetic Algorithms in Search, Optimization, and Machine Learning.’ (Addison-Wesley: Reading)
Grabner KG, Dwyer JP, Cutter BE (1997) Validation of BEHAVE fire behavior predictions in oak savannas using five fuel models. In ‘Proceedings Eleventh Central Hardwood Conference University of Missouri.’ (Eds SG Pallardy, RA Cecih, HG Garrett, PS Johnson). USDA Forest Service, General North Central Forest Experiment Station, Technical Report NC-188, pp. 202–215. (St. Paul, MN)
Grabner KW, Dwyer JP, Cutter BE (2001) Fuel model selection for BEHAVE in Midwestern savannas. Northern Journal of Applied Forestry 18, 74–80.
Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 16, 122–128.
| Optimization of control parameters for genetic algorithms.Crossref | GoogleScholarGoogle Scholar |
Holland J (1975) ‘Adaptation in Artificial and Natural Systems.’ (The University of Michigan Press: Ann Arbor)
Hough WA, Albini FA (1978) Predicting fire behavior in palmetto-gallberry fuel complexes. USDA Forest Service, Southern Research Station, Research Paper SE-174. (Asheville, NC)
Jolly WM (2007) Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture. International Journal of Wildland Fire 16, 503–509.
| Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture.Crossref | GoogleScholarGoogle Scholar |
Lautenberger C, Rein G, Fernandez-Pello C (2006) The application of a genetic algorithm to estimate material properties for fire modeling from bench-scale fire test data. Fire Safety Journal 41, 204–214.
| The application of a genetic algorithm to estimate material properties for fire modeling from bench-scale fire test data.Crossref | GoogleScholarGoogle Scholar |
Lonati M, Gorlier A, Ascoli D, Marzano R, Lombardi G (2009) Response of the alien species Panicum acuminatum to disturbance in an Italian lowland heathland. Botanica Helvetica 119, 105–111.
| Response of the alien species Panicum acuminatum to disturbance in an Italian lowland heathland.Crossref | GoogleScholarGoogle Scholar |
Lopes A, Cruz M, Viegas D (2002) Fire station an integrated software system for the numerical simulation of fire spread on complex topography. Environmental Modelling & Software 17, 269–285.
| Fire station an integrated software system for the numerical simulation of fire spread on complex topography.Crossref | GoogleScholarGoogle Scholar |
Ohenoja M, Leiviskä K (2010) Validation of genetic algorithm results in a fuel cell model. International Journal of Hydrogen Energy 35, 12 618–12 625.
| Validation of genetic algorithm results in a fuel cell model.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtlansbzM&md5=a09727c8c0a2460f64552594f4792b8dCAS |
Prichard SJ, Sandberg DV, Ottmar RD, Eberhardt E, Andreu A, Eagle P, Swedin K (2013) Fuel Characteristic Classification System version 3.0: technical documentation. USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-887. (Portland, OR)
R Core Development Team (2013) R: A language and environment for statistical computing. Version 2.15. (R Foundation for Statistical Computing: Vienna, Austria).
Reinhardt ED, Crookston NLL (2003) The Fire and Fuels Extension to the Forest Vegetation Simulator. USDA Forest Service, Rocky Mountain Research Station, Technical Report RMRS-GTR-116. (Fort Collins, CO)
Robinson AP (2013) Package ‘equivalence’. Available at: cran.r-project.org/web/packages/equivalence/equivalence.pdf [Verified 28 September 2014].
Robinson AP, Duursma RA, Marshall JD (2005) A regression-based equivalence test for model validation: shifting the burden of proof. Tree Physiology 25, 903–913.
| A regression-based equivalence test for model validation: shifting the burden of proof.Crossref | GoogleScholarGoogle Scholar | 15870057PubMed |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Technical Report INT-GTR-115. (Ogden, UT)
Rothermel RC, Rinehart GC (1983) Field procedures for verification and adjustment of fire behavior predictions. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-142. (Ogden, UT)
Sandberg DV, Riccardi CL, Schaaf MD (2007) Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbeds. Canadian Journal of Forest Research 37, 2438–2455.
| Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbeds.Crossref | GoogleScholarGoogle Scholar |
Santana VM, Marrs RH (2014) Flammability properties of British heathland and moorland vegetation: models for predicting fire ignition. Journal of Environmental Management 139, 88–96.
| Flammability properties of British heathland and moorland vegetation: models for predicting fire ignition.Crossref | GoogleScholarGoogle Scholar | 24681648PubMed |
Schaaf MD, Sandberg DV, Schreuder MD, Riccardi CL (2007) A conceptual framework for ranking crown fire potential in wildland fuelbeds. Canadian Journal of Forest Research 37, 2464–2478.
| A conceptual framework for ranking crown fire potential in wildland fuelbeds.Crossref | GoogleScholarGoogle Scholar |
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, Technical Report RMRS-GTR-153. (Fort Collins, CO)
Scrucca L (2013) GA: a package for genetic algorithms in R. Journal of Statistical Software 53, 1–37.
Simard AJ, Eenigenburg JE, Adams KA, Nissen RL, Deacon AG (1984) A general procedure for sampling and analyzing wildland fire spread. Forest Science 30, 51–64.
Sneeuwjagt RJ (1974) Evaluation of the grass fuel model of the National Fire Danger rating System. MSc Thesis. University of Washington, Seattle.
Sneeuwjagt RJ, Frandsen WH (1977) Behavior of experimental grass fires vs. predictions based on Rothermel’s fire model. Canadian Journal of Forest Research 7, 357–367.
| Behavior of experimental grass fires vs. predictions based on Rothermel’s fire model.Crossref | GoogleScholarGoogle Scholar |
Spielmann M (2009) Fuel load assessment methods in forest and non-forest vegetation in Central Europe: a comparative analysis. International Forest Fire News 38, 80–84.
Stockwell DRB, Noble IR (1990) Induction of sets of rules from animal distribution data: a robust and informative method of data analysis. Mathematics and Computers in Simulation 32, 249–254.
| Induction of sets of rules from animal distribution data: a robust and informative method of data analysis.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
| Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |
Vacchiano G, Ascoli D (2014) An implementation of the Rothermel fire spread model in the R programming language. Fire Technology
| An implementation of the Rothermel fire spread model in the R programming language.Crossref | GoogleScholarGoogle Scholar |
Vacchiano G, Motta R, Bovio G, Ascoli D (2014) Calibrating and testing the forest vegetation simulator to simulate tree encroachment and control measures for heathland restoration in Southern Europe. Forest Science 60, 241–252.
| Calibrating and testing the forest vegetation simulator to simulate tree encroachment and control measures for heathland restoration in Southern Europe.Crossref | GoogleScholarGoogle Scholar |
van Wilgen BW (1984) Adaptation of the United States Fire Danger Rating System to fynbos conditions. Part I. A fuel model for fire danger rating in the fynbos byome. South African Forestry Journal 129, 61–65.
| Adaptation of the United States Fire Danger Rating System to fynbos conditions. Part I. A fuel model for fire danger rating in the fynbos byome.Crossref | GoogleScholarGoogle Scholar |
van Wilgen BW, Le Maitre DC, Kruger FJ (1985) Fire behaviour in South African fynbos (macchia) vegetation and predictions from Rothermel’s fire model. Journal of Applied Ecology 22, 207–216.
| Fire behaviour in South African fynbos (macchia) vegetation and predictions from Rothermel’s fire model.Crossref | GoogleScholarGoogle Scholar |
Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resources Research 27, 2467–2471.
| The genetic algorithm and its application to calibrating conceptual rainfall-runoff models.Crossref | GoogleScholarGoogle Scholar |
Wendt K, Cortes A, Margalef T (2013) Parameter calibration framework for environmental emergency models. Simulation Modelling Practice and Theory 31, 10–21.
| Parameter calibration framework for environmental emergency models.Crossref | GoogleScholarGoogle Scholar |
Wu ZW, He HS, Chang Y, Liu ZH, Chen HW (2011) Development of customized fire behavior fuel models for boreal forests of northeastern China. Environmental Management 48, 1148–1157.
| Development of customized fire behavior fuel models for boreal forests of northeastern China.Crossref | GoogleScholarGoogle Scholar | 21691875PubMed |
