1. Carlos Brun, Tomàs Margalef and Ana Cortés Computer Architecture and Operating Systems Dept. Universitat Autònoma de Barcelona (Spain) Coupling Diagnostic and Prognostic Models to a Dynamic Data Driven Forest Fire Spread Prediction System

2. Experimentation Two-stage methodology Conclusions & Future work Coupling models Forest fire prediction P P’ Model P’ Fire Simulator WsWd … MTX Prediction t i+1 titi titi titi

3. Forest fires in Europe Most affected countries in Europe  Environmental impact  Loss of human lives  Economic expenses in prevention and extinction Experimentation Two-stage methodology Conclusions & Future work Coupling models

4. Index Introduction Two-stage prediction methodology Coupling complementary models Experimentation Conclusions & future work

5. Classical prediction vs Two-stage prediction titi RFt i RFt i+1 RFt i+2 t i+2 t i+1 FARSITE  Parameters Calibration FARSITE SFt i+2 SFt i+1 CALIBRATION STAGE PREDICTION STAGE Parameters imprecision & uncertainty -The search is driven by observed real front -> DDDAS paradigm - Working hypothesis: the conditions remain quite stable between stages Experimentation Introduction Conclusions & Future work Coupling models

6. Prescribed fires Area: Hundreds of m 2. Time: Minutes/a few hours. Regular terrain Controlled conditions. Real fires Area: Hundreds of ha. Time: Days. Complex terrain NOT controlled conditions. Experimentation Introduction Conclusions & Future work Coupling models

7. WsWd … MTX Wind model Variables such as wind, humidity and temperature, among others, are considered uniform throughout the terrain. Spatial distribution of parameters Methodology restrictions: WindNinja Experimentation Introduction Conclusions & Future work Coupling models

8. Time t x +  tt x + 2  tt x + 3  t txtx t x+1 Input parameters t i Meteorological model Weather forecast for t i +  t Weather forecast for t i + 2  t Weather forecast for t i + 3  t Real front in t x Simulated front in t x+1 The parameters that define fire behavior are considered constant throughout the prediction interval. Temporal distribution of the parameters Methodology restrictions: Experimentation Introduction Conclusions & Future work Coupling models Fire simulator Weather forecast for t i + 3  t Fire simulator

9. Objectives: Coupling complementary models to minimize prediction errors in real scenarios. Study these approaches and compare their results under changing conditions Analyze calibration and prediction errors depending on models coupled. Analyze how soft and hard changes in conditions affect the accuracy of every approach. Experimentation Two-stage methodology Introduction Conclusions & Future work

10. Wind model Fire simulator Error calculation C++/MPI Application SYSTEM INTERFACE Population generator Prediction framework ExperimentationBackground Introduction Conclusions & Future work Meteorological model Calibration stage Genetic Algorithm

11. Fire simulator RFt i Fire simulator RFt i SFt i+1 Wind model RFt i+1 SFt i+2 population x Evolved population x+1 EC 2ST-BASIC2ST-WF2ST-MM Wind model 2ST-MM-WF Coupling models to improve 2-stage methodology Real observations Predicted data Meteorological model Experimentation Two-stage methodology Introduction Conclusions & Future work

12. Experimentation: Coupling models to improve 2-stage methodology 1. 2-Stage basic (2ST-BASIC) 2. 2-Stage with Wind Field model (2ST-WF) 3. 2-Stage with Meteorological Model data injection (2ST-MM) 4. 2-Stage with Wind Field and Meteorological model (2ST-MM-WF) - Compare their behavior under certain terrain and meteorological conditions. Two-stage methodology Introduction Conclusions & Future work Coupling models

13. Experimentation: Terrain used in this experimentation is located in Cap de Creus Two-stage methodology Introduction Conclusions & Future work Coupling models

14. Error = (Cells( ∪ ) – Cells(ini)) – (Cells( ∩ ) – Cells(ini)) Cells(real) – Cells(ini) Error is the normalized symmetric difference between maps:  Reference fire is a synthetic fire evolved over this terrain during 18 hours  There has been done 2 calibration and 2 prediction steps  4 methodologies use GA with a random initial populations of 50 individuals  Terrain moistures and meteorological conditions of reference are considered unknown Two-stage methodology Introduction Conclusions & Future work Coupling models Experimentation:

15. Hard and soft changes in conditions speed (mph) time (h) Two-stage methodology Introduction Conclusions & Future work Coupling models

16. Coupling models to improve 2-stage methodology Calibration from 0 to 6 hours and prediction from 6 to 12. Conditions suffer a sudden change between stages 2 ST-BASIC and 2 ST-WF are not capable to be sensitive to this change. time(h) 0 18612calibration prediction conditions error Two-stage methodology Introduction Conclusions & Future work Coupling models

17. Coupling models to improve 2-stage methodology Calibration from 6 to 12 hours and prediction from 12 to 18. Conditions suffer a soft change between stages 2 ST-BASIC and 2 ST-WF behave better in this case. Although this, 2 ST-MM and 2 ST-MM-WF do a better prediction. time(h) 0 18612calibration prediction conditions error Two-stage methodology Introduction Conclusions & Future work Coupling models

18. Coupling models to improve 2-stage methodology Two-stage methodology Introduction Conclusions & Future work Coupling models

19. Fire models parameters are difficult to know or even estimate so calibration techniques are interesting to reduce this uncertainly. There have been studied and compared 4 methodologies which combine models and improve fire spread prediction. Prognostic and diagnostic models allows us to have more precise information to our system. These models introduce a computational overhead that must be tackled. It must be performed a deeper analysis working with more terrains, different conditions and GA configurations Experimentation Two-stage methodology Introduction Coupling models Conclusions and future work

20. 2011-2012 Brun, C., Artés, T., Margalef, T., Cortés, A.: Coupling wind dynamics into a dddas forest fire propagation prediction system. Procedia Computer Science 9(0) (2012) 1110 – 1118 Proceedings of the International Conference on Computational Science, ICCS 2012 2012-2013 Brun, Carlos, Margalef, Tomàs, Cortés, Anna, Acoplando modelos complementarios para la mejora de un sistema de predicción de incendios forestales, Jornadas Sarteco, JP2012. Brun, C., Margalef, T., Cortés, A.: Coupling Diagnostic and Prognostic Models to a Dynamic DataDriven Forest Fire Spread Prediction System. ICCS 2013 (accepted) Brun, C., Sanjuan, G., Margalef, T., Cortés, A., Exploting multi-core parallelism to accelerate wind calculation in forest fire spread prediction, International Conference on Parallel Processing and Applied Mathematics, PPAM 2013 (waiting for reply). CMMSE 2013 (finishing paper) ExperimentationBackgroundIntroduction Current work Publications

21. Thank you for your attention! Questions…