Ascoli et al. 2016
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A new tool in R to calibrate Rothermel fire behavior fuel models by genetic algorithm optimization
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Ascoli et al. 2016
- 1. A new tool in to calibrate Rothermel fire behavior fuel models by genetic algorithm optimization Medfirelab 13 December 2016, Rome, Italy Università di Napoli Federico II Davide Ascoli Università di Torino Giovanni Bovio JRC EC Unit D1 Bioeconomy Giorgio Vacchiano
- 2. ROS = IR ξ (1+Φw+Φs) ρb є Qig Introduction Medwildfirelab - Global Change Impacts on Wildland Fire Behaviour and Uses in Mediterranean Forest Ecosystems Rome, Italy 13 Dec, 2016 Davide Ascoli dascoli7@gmail.com The Rothermel model
- 3. BehavePlus Farsite Flammap Wildfire Analyst Forest Vegetation Simulator ArcFuels Rothermel model Introduction Medwildfirelab - Global Change Impacts on Wildland Fire Behaviour and Uses in Mediterranean Forest Ecosystems Rome, Italy 13 Dec, 2016 Davide Ascoli dascoli7@gmail.com The Rothermel model
- 4. Fuel moisture Wind speed Slope Load SA/V Heat content Fuel depth Moist extinction Min Max ROS = IR ξ (1+Φw+Φs) ρb є Qig Introduction The fuel model concept
- 5. The fuel model concept Albini 1976 Estimating wildfire behavior and effects INT-GTR-30 Scott & Burgan 2005 Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s fire spread model RMRS-GTR-153 13 40 Load SA/V Heat content Fuel depth Moist extinction Min Standard fuel models Max Introduction ? ? ? ? ?
- 6. PredictedROS Observed ROSScott & Burgan 2005 Grass-Shrub – GS3 Introduction Load SA/V Heat content Fuel depth Moist extinction Min Max Custom fuel models
- 7. PredictedROS Observed ROS Load SA/V Heat content Fuel depth Moist extinction Min Max Scott & Burgan 2005 Grass-Shrub – GS3 Custom fuel models Introduction
- 8. Fuel inventory Load SA/V Heat content Fuel depth Moist extinction Min Max PredictedROS Observed ROS Introduction average Custom fuel models
- 9. Subjective calibration Load SA/V Heat content Fuel depth Moist extinction Min Max Introduction PredictedROS Observed ROS average Shortcomings - Not reproducible - Time consuming - Not easy to guess what … Fuel inventory OK fitness
- 10. Mathematical optimization Load SA/V Heat content Fuel depth Moist extinction Min Max Smallest RMSE PredictedROS Observed ROS "Backtracking calibration procedure which systematically calculates several possible solutions from a set of input combinations… …The criterion for the best prediction is the smallest root mean square error (RMSE)" Introduction
- 11. Introduction Heathland fuel model Scotland ItalyGermany How to calibrate a custom fuel model to plan prescribed burning in European heathlands? Photo: Held A.
- 13. Load SA/V Heat content Fuel depth Moist extinction Min Max Genetic algorithms set 1 set 2 set N Random sampling set 1 set 2 set 3 Initial population parameters sets Pop. size N = 3 Fitness evaluation The model is run with each parameter set. Fitness (e.g., RMSE) between ROS predictions and observations is computed. Sets are ranked by their fitness High fitness Medium fitness Low fitness RMSE set 2 set 1 set 3 Introduction
- 14. Selection Parameter sets are sampled according to a probability proportional to their ranking set 1 set 2 set 2 Introduction Load SA/V Heat content Fuel depth Moist extinction Min Max Genetic algorithms Fitness evaluation The model is run with each parameter set. Fitness (e.g., RMSE) between predictions and observations is computed. Sets are ranked by their fitness High fitness Medium fitness Low fitness RMSE set 2 set 1 set 3 Introduction
- 15. Selection Parameter sets are sampled according to a probability proportional to their ranking set 1 set 2 set 2 Introduction Load SA/V Heat content Fuel depth Moist extinction Min Max Genetic algorithms Introduction Crossing over Sets are randomly selected in pairs. Parameters are swapped set 1.1 set 2 set 2.1
- 16. Load SA/V Heat content Fuel depth Moist extinction Min Max Mutation Sets are randomly selected. Parameters are randomly resampled from the initial range set 1.1 set 2.1 set 2.1.1 Introduction Crossing over Sets are randomly selected in pairs. Parameters are swapped set 1.1 set 2 set 2.1 Genetic algorithms Introduction
- 17. Mutation Sets are randomly selected. Parameters are randomly resampled from the initial range set 1.1 set 2.1 set 2.1.1 New generations until … Minimum fitness threshold, or Fixed number of iterations reached, or Fixed number of iterations reached without fitness improvement Load SA/V Heat content Fuel depth Moist extinction Min Max Introduction Genetic algorithms Introduction Iterations RMSE
- 18. Introduction How to do that?... 5
- 19. Vacchiano & Ascoli 2014 An Implementation of the Rothermel Fire Spread Model in the R Programming Language Fire Technology Uncertainty analysis The Rothermel Package for R Introduction CRAN + Rothermel model 1972 (imperial units) after Albini (1976); dynamic fuel transfer; no wind speed limit + Package Functions
- 20. Vacchiano & Ascoli 2014 An Implementation of the Rothermel Fire Spread Model in the R Programming Language Fire Technology RMSE Uncertainty analysis The Rothermel Package for R Introduction CRAN + Rothermel model 1972 (imperial units) after Albini (1976); dynamic fuel transfer; no wind speed limit + Package Functions Best standard FM
- 21. Vacchiano & Ascoli 2014 An Implementation of the Rothermel Fire Spread Model in the R Programming Language Fire Technology Ascoli et al. 2015 Building Rothermel fire behaviour fuel models by genetic algorithm optimisation International Journal of Wildland Fire RMSE Uncertainty analysis The Rothermel Package for R Introduction CRAN + Rothermel model 1972 (imperial units) after Albini (1976); dynamic fuel transfer; no wind speed limit + Package Functions Best standard FM GA-Roth
- 22. Introduction GA-Roth settings + Population size = 50 + Crossing over prob. = 0.8+ Mutation prob. = 0.1 + Number generations = 50+ Computation time = 52 seconds - IntelCorei5, RAM8Gb Ascoli et al. 2015
- 23. (1) Test if optimization by Genetic Algorithms (GA) improves the accuracy of previously published custom fuel models calibrated using other methods (2) Use GA to calibrate a fuel model for heathlands (GA-heath), using fuel, weather, and fire behaviour data measured under experimental conditions (3) Evaluate the performance of GA-heath against standard fuel models and a custom fuel model built using average heath fuels characteristics Introduction Objectives
- 24. Wind direction Wind speed Load SA/V HeatDepth Mx Max Min Methods Objective 1: GA vs. published custom fuel models (i) a dataset of observed ROS (ii) measures of wind speed, slope, fuel moisture for each ROS (iii) a dataset of inventory / laboratory fuel characteristics (iv) a custom fuel model calibrated using observed ROS We searched for studies with … ROS ROS ROS
- 25. Fuel Type LITTER GRASS SHRUB Selected studies Grabner et al. 1997 Grabner et al. 2001 Sneeuwjagt 1974 Sneeuwjagt et al. 1977 van Wilgen 1984 van Wilgen et al. 1985 Fitness Statistics Pub. GA Pub. GA Pub. GA RMSE (m min-1) 5.0 3.0 5.4 4.3 7.2 5.5 MAE (m min-1) 3.9 2.0 4.1 2.9 6.2 4.3 MAPE (%) 128 54 252 126 30 20 MBE (m min-1) 3.2 0.1 2.7 0.6 2.1 -0.4 Results Objective 1: GA vs. published custom fuel models
- 26. Methods Objective 2: GA-heath model calibration (i) we created a dataset of observed ROS We carried out 9 fire experiments … Exp. 7 25-50 m 50-80 m
- 27. 40 ROS 1 - 26 m min-1 Methods Objective 2: GA-heath model calibration (i) we created a dataset of observed ROS (Simard et al. 1984) We carried out 9 fire experiments … 25-50 m 50-80 m ROS ROS ROS ROS ROS ROS
- 28. Wind direction Wind speed Load SA/V HeatDepth Mx Max Min Methods Objective 2: GA-heath model calibration (i) 40 ROS: 20 calibration + 20 validation dataset (ii) measures of fuel moisture, wind speed, slope for each ROS (iii) a dataset of inventory / laboratory fuel characteristics (iv) we used GA-Roth function to calibrate a custom fuel model We carried out 9 fire experiments …
- 29. Results Objective 2: GA-heath model calibration Fitness Statistics Cal. Val. RMSE (m min-1) 1.7 1.8 MAE (m min-1) 1.3 1.4 MAPE (%) 20 32 MBE (m min-1) 0.1 0.5 Observed rate of spread (m/min) Predictedrateofspread(m/min)
- 30. RMSE Methods Objective 3: Validation: GA-heath, standard, custom-aver 1) Standard fuel model selection: grass-shrub (GS) group Rothermel Package Function Best standard FM GS3 2) Custom fuel model* using average inventoried fuels (custom-aver) Load SA/V Heat content Fuel depth Moist extinction Min Max average *Vacchiano et al. 2014 Calibrating and testing FVS… Forest Science
- 31. Results Objective 3: Validation: GA-heath, standard, custom-aver GA optimization reduced underprediction
- 32. Results Objective 3: GA-heath vs. custom-average
- 33. Conclusions Improvements to the state of art Optimization by Genetic Algorithms (GA) improved the accuracy of previously published custom fuel models GA explores a continuous search space, is reproducible, is computational effective, not require fuel inventory (min-max) Is a viable method to calibrate custom fuel models and could be implemented in fire modelling systems To test GA optimization, we designed the GA-Roth ( ) function in the Rothermel Package for R Medwildfirelab - Global Change Impacts on Wildland Fire Behaviour and Uses in Mediterranean Forest Ecosystems Rome, Italy 13 Dec, 2016 Davide Ascoli dascoli7@gmail.com
- 34. Conclusions CRAN + Rothermel Package for R downloads in 2015-2016 Vacchiano & Ascoli 2014 An Implementation of the Rothermel Fire Spread Model in the R Programming Language Fire Technology Numberofdownloads 20 Jan 2015 Apr 2015 Jul 2015 Oct 2015 10 30 0 Applications…
- 35. Conclusions Applications… Medwildfirelab - Global Change Impacts on Wildland Fire Behaviour and Uses in Mediterranean Forest Ecosystems Rome, Italy 13 Dec, 2016 Davide Ascoli dascoli7@gmail.com UTAD UNINA CSIRO MEA
- 36. Conclusions Applications… Medwildfirelab - Global Change Impacts on Wildland Fire Behaviour and Uses in Mediterranean Forest Ecosystems Rome, Italy 13 Dec, 2016 Davide Ascoli dascoli7@gmail.com
- 37. …take a breath THANKS FOR THE ATTENTION Medwildfirelab - Global Change Impacts on Wildland Fire Behaviour and Uses in Mediterranean Forest Ecosystems Rome, Italy 13 Dec, 2016 Davide Ascoli dascoli7@gmail.com View publication statsView publication stats