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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. Agenda. Problem statement Overview SAPIFE³rt - Real time data injection

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slide1

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

agenda
Agenda
  • Problem statement
  • Overview
  • SAPIFE³rt - Real time data injection
  • Policy for data injection
  • Experiments
  • Conclusions and Future Work
problem statement
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.
problem statement1
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……
research goals
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

slide6

Two Stages Propagation Prediction

Simulador

Simulador

Prediction Stage

Parameters

Calibration Stage

Hypothesis: the environmental conditions are similar in the two stages

calibration stage sapife

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

slide8

Method Evaluations

California Fires

Catalunya Fires

Greece Fires

Real Fires

Prescribed Fires

Synthetic Fires

Error Ratio

slide9

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

slide10

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

slide11

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

calibration stage sapife rt

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

data injection
Data injection

360º

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

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

slide14

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

slide15

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

slide16

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

freeway complex fire
Freeway Complex Fire
  • Injection map every 60 min
  • Injection wind data every 5 min
slide18

Results

CFV_threshold= 2.5

conclusions
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.
slide20

Applying DDDAS Concept

Feedback

speed

SAPIFE³rt

time

  • Weather Stations
  • Remote Sensing
  • Output
  • Drive Process
  • + or – frequency
  • + or - precision
  • Input Parameters
  • Monitoring

Measurements

speed

time

slide22

Results

  • Injection map every 30 min
  • Injection wind data every 5 min
  • Wind samples data for CFV estimation is 3
slide23

Policies for Data Injection

corr=0.97

CFV_threshold=1.5

slide24

Results

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

CFV_threshold= 3.0

slide25

Results

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

CFV_threshold= 3.0

slide26

Results

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