Universitat Autònoma de Barcelona, España Roque Rodríguez, Ana Cortés and Tomás Margalef

1. Towards policies for data insertion in dynamic data driven application systems: a case study sudden changes in wildland fire Universitat Autònoma de Barcelona, España Roque Rodríguez, Ana Cortés and Tomás Margalef

2. Agenda • Problem statement • Overview • SAPIFE³rt - Real time data injection • Policy for data injection • Experiments • Conclusions and Future Work

3. Problem statement • Forest fires are one of the most worrisome natural disasters, destroying thousands of acres of forests and nearby urban zones, affecting plant, animal and human life. • The forest fires are a fact of nature, and have been serving as means of self-regulation of forests. However, these phenomena have become more frequent during the last years.

4. Problem statement • Fire propagation simulators are a very useful tool to help combat forest fires. • Those are based on mathematical and physic models, and with their help, we can mitigate the damage, optimize resources and save lives. But……

5. Research Goals • Improve prediction results. “it is a paradigm whereby application/simulations and measurements become a symbiotic feedback control system. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse the ability of an application to dynamically steer the measurement process” Dra. Frederica Darema • Reduce execution time. • Inject data at execution time. Applying Dynamic Data Driven Applications Systems concept

6. Two Stages Propagation Prediction Simulador Simulador Prediction Stage Parameters Calibration Stage Hypothesis: the environmental conditions are similar in the two stages

7. Genetic Algorithm Population New population Best Probability Selection Generation Individual A Individual B Bcp1 Bcp2 Acp1 Acp2 Cross Elitism Child AB1 Child AB2 Acp1 Bcp2 Bcp1 Acp2 Mutation scenarios=individuals Calibration Stage: SAPIFE³ FireSim Best Population FireSim 2 2 S F M FireSim FireSim

8. Method Evaluations California Fires Catalunya Fires Greece Fires Real Fires Prescribed Fires Synthetic Fires Error Ratio

9. Fire Evolution Analysis Hypothesis: the environmental conditions are similar in the two stages Fire Spread Evolution 4 to 6 min Fire Spread Evolution 6 to 8 min

10. Fire Evolution Analysis Hypothesis: the environmental conditions are similar in the two stages Fire Spread Evolution 10 to 12 min Fire Spread Evolution 12 to 14 min

11. SAPIFE³ Real time Data base Photo Image Training Data Data Stream Data Collection & Processing System Fire Manager Dynamically Injected Data Input Parameters Satellite Image Simulator Simulator Fire Simulated Weather Station Genetic Algorithm Statistical Method Weather Balloon Urgent HPC

12. Genetic Algorithm Population New population Probability Selection Generation Individual A Individual B Bcp1 Bcp2 Acp1 Acp2 Cross Elitism Child AB1 Child AB2 Acp1 Bcp2 Bcp1 Acp2 Mutation scenarios=individuals Calibration Stage: SAPIFE³rt FireSim Best Population FireSim 2 2 S F M FireSim FireSim

13. Data injection 360º 0º 0mph 50º 11 40º 7 45º 9 20mph WindSpeed bounded range WindDir bounded range WindDir 45 WindSpeed 9 Weather Station Weather Balloon Best Population 20mph 360º 0º 0mph 7 0.99 0.00 8.00 78.00 0.00 21.00 7 0.99 0.00 4.00 37.00 0.00 21.00 7 0.99 0.00 3.00 45.00 0.00 21.00 WindDir valid range WindSpeed valid range 7 0.99 0.00 7.00 34.00 0.00 21.00 7 0.99 0.00 5.00 40.00 0.00 21.00 7 0.99 0.00 9.00 38.00 0.00 21.00 7 0.99 0.00 5.00 42.00 0.00 21.00 7 0.99 0.00 4.50 37.00 0.00 21.00 7 0.99 0.00 6.50 39.00 0.00 21.00 7 0.99 0.00 5.00 25.00 0.00 21.00

14. Data Injection Map Map Map Map 10 Wind Speed 3 GA S GA S X X X X X X X X X X X X X X X X X X X X X X X X X 23 9 6 2 24 10 14 20 1 8 15 22 21 3 4 5 11 18 13 7 19 16 25 17 12 P P Prediction Prediction GA S

15. Policy for Data Injection Change Factor of a given Variable (CFV ) Prediction Stage Calibration Stage Prediction Stage speed time c p   p c speed l l l X X X time CFV Changes in the behavior of this variable is negligible speed  time

16. CFV Estimation Map Map Map Map 10 Wind Speed 3 GA S GA S X X X X X X X X X X X X X X X X X X X X X X X X X 23 9 6 2 24 10 14 20 1 8 15 22 21 3 4 5 11 18 13 7 19 16 25 17 12 P P Prediction Prediction GA S

17. Freeway Complex Fire • Injection map every 60 min • Injection wind data every 5 min

18. Results CFV_threshold= 2.5

19. Conclusions • We observed that data injection in real time can improve the prediction results significantly when conditions are dynamic and changes are sudden. • We gain time and flexibility for changing situations. • We also conclude that the data acquisition frequency directly affects the prediction results, as well as the precision on the detection of sudden changes.

20. Applying DDDAS Concept Feedback speed SAPIFE³rt time • Weather Stations • Remote Sensing • Output • Drive Process • + or – frequency • + or - precision • Input Parameters • Monitoring Measurements speed time

21. Thank You!!!

22. Results • Injection map every 30 min • Injection wind data every 5 min • Wind samples data for CFV estimation is 3

23. Policies for Data Injection corr=0.97 CFV_threshold=1.5

24. Results • Injection map every 30 min • Injection wind data every 5 min • Wind samples data for CFV estimation is 3 CFV_threshold= 3.0

25. Results • Injection map every 30 min • Injection wind data every 5 min • Wind samples data for CFV estimation is 3 CFV_threshold= 3.0

26. Results • Injection map every 60 min • Injection wind data every 5 min • Wind samples data for CFV estimation is 6