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…