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. Improve prediction results. Research Goals Reduce execution time. Inject data at execution time. Applying Dynamic Data Driven Applications Systems concept “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

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

7. Calibration Stage: SAPIFE³ Cross Genetic Algorithm Selection Mutation Individual B Bcp1Bcp2 Individual A Acp1Acp2 Child AB1 Acp1Bcp2 Child AB2 Bcp1Acp2 Population New population Elitism Generation scenarios=individuals Best Population S 2 F 2 M Best Probability FireSim

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

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

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

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

12. Calibration Stage: SAPIFE³rt Cross Genetic Algorithm Selection Mutation Individual B Bcp1Bcp2 Individual A Acp1Acp2 Child AB1 Acp1Bcp2 Child AB2 Bcp1Acp2 Population New population Elitism Generation scenarios=individuals Best Population S 2 F 2 M Probability FireSim

13. 0º 360º 40º 45º 50º WindDir bounded range 0º 360º WindDir valid range Best Population Data injection Weather Station Weather Balloon 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 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 WindDir 45 WindSpeed 9 0mph 20mph WindSpeed valid range 0mph 20mph 7 9 11 WindSpeed bounded range

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

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

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

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. Output Drive Process + or – frequency + or - precision Input Parameters Monitoring Weather Stations Remote Sensing MeasurementsSAPIFE³rt time speed time speed Applying DDDAS Concept

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