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…