Carlos Brun, Tomàs Margalef and Ana Cortés Computer Architecture and Operating Systems Dept....
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Transcript of Carlos Brun, Tomàs Margalef and Ana Cortés Computer Architecture and Operating Systems Dept....
Carlos Brun, Tomàs Margalef and Ana CortésComputer 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
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
Forest fire prediction
P
P’
ModelP’
Fire Simulator
Ws Wd …M T X Prediction ti+1
ti
ti
ti
Forest fires in EuropeMost affected countries in Europe
Environmental impact Loss of human lives Economic expenses in prevention and extinction
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
Index
• Introduction• Two-stage prediction
methodology• Coupling complementary
models• Experimentation• Conclusions & future work
Classical prediction vs Two-stage predictionti
RFti RFti+1 RFti+2
ti+2ti+1
FARSITE
e?
Parameters
Calibration
FARSITE SFti+2
SFti+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
ExperimentationTwo-stage
methodologyIntroduction Conclusions &
Future workCoupling models
Prescribed firesArea: Hundreds of m2.Time: Minutes/a few
hours.Regular terrainControlled conditions.
Real firesArea: Hundreds of ha.Time: Days.Complex terrainNOT controlled
conditions.
ExperimentationTwo-stage
methodologyIntroduction Conclusions &
Future workCoupling models
Ws Wd …M T X Wind model
Variables such as wind, humidity and temperature, among others, are considered uniform throughout the terrain.
Spatial distribution of parameters
Methodology restrictions:
WindNinja
ExperimentationTwo-stage
methodologyIntroduction Conclusions &
Future workCoupling models
Time
tx + Dt tx + 2Dt tx + 3Dt tx
tx+1
Input parameters ti
Meteorological model
Weather forecast for ti
+ Dt
Weather forecast for ti
+ 2Dt
Weather forecast for ti
+ 3Dt
Real front in tx
Simulated front in tx+1
The parameters that define fire behavior are considered constant throughout the prediction interval.
Temporal distribution of the parameters
Methodology restrictions:
ExperimentationTwo-stage
methodologyIntroduction Conclusions &
Future workCoupling models
Fire simulator
Fire simulator
Fire simulator
Weather forecast for ti
+ 3Dt
Fire simulator
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.
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
Fire simulator
RFti
Fire simulator
Fire simulator
Fire simulator
RFti
RFti
SFti+1
SFti+1
SFti+1Wind model
Wind model
Wind model
Wind model
RFti+1
SFti+2
populationxEvolved population x+1
EC
EC
EC
2ST-BASIC2ST-WF2ST-MM
Wind model
Wind model
2ST-MM-WF
Coupling models to improve 2-stage methodology
Real observations
Predicted data
Meteorological model
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
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.
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
Experimentation:
• Terrain used in this experimentation is located in Cap de Creus
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
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
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
Experimentation:
Experimentation:
• Hard and soft changes in conditions
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180
2.5
5
7.5
10
12.5
15
17.5
20
Real wind speed
Real wind speed
spee
d (m
ph)
time (h)
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
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• 2ST-BASIC and 2ST-WF are not capable to be sensitive to this
change.
time(h) 0 186 12calibration prediction
conditions
err
or
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
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• 2ST-BASIC and 2ST-WF behave better in this case. • Although this, 2ST-MM and 2ST-MM-WF do a better prediction.
time(h) 0 186 12calibration prediction
conditions
err
or
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
Coupling models to improve 2-stage methodology
ExperimentationTwo-stage methodologyIntroduction Conclusions &
Future workCoupling models
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
ExperimentationTwo-stage methodologyIntroduction
Conclusions & Future work
Coupling models
Conclusions and future work
Thank you for your attention!
Questions…