jong gyu han, keun ho ryu, kwang hoon chi and yeon kwang yeon
DESCRIPTION
Statistics Based Predictive Geo-Spatial Data Mining: Forest Fire Hazardous Area Mapping Application. Jong Gyu Han, Keun Ho Ryu, Kwang Hoon Chi and Yeon Kwang Yeon. Problem Definition. -Forrest Fire Prevention Finding spatial-temporal distribution of forest fires - PowerPoint PPT PresentationTRANSCRIPT
26-10-2003 1
Statistics Based Predictive Geo-Spatial Data Mining: Forest Fire Hazardous Area Mapping
Application
Jong Gyu Han, Keun Ho Ryu, Kwang Hoon Chi and Yeon Kwang Yeon
26-10-2003 2
Problem Definition
-Forrest Fire Prevention
-Finding spatial-temporal distribution of forest fires
-Predicting forest fire hazardous areas from large spatial data sets
-Leads to a forest fire hazard prediction model
26-10-2003 3
Problem Definition 2
Youngdong Region of Kangwan Province, Republic of Korea
Using:
-Historical data on fire ignition point locations
-Grid-based multi-layer GIS
26-10-2003 4
Prediction Methods
Depends on relationship of spatial data sets relevant to forest fire with respect to areas of previous forest fire ignition
N[S] = all area
N[F] = fire ignition areas
N[A] = forest type A
N[E] = area of fire ignition on forest type A
26-10-2003 5
Conditional Probability Prediction Model
Average density of ignition areas:
P(F) = N[F]/N[S]
Without other information this is the probability of a forest fire ignition area
Favourability of finding a forest ignition area given the presence of forest type A:
CondP(F\A) = P(A\F) · P(F)
P(A)
P(A\F) = P(A ∩ F)
P(F)
P(A ∩ F) = N[A ∩ F] / N[S] = N[E] / N[S]
26-10-2003 6
Conditional Probability Prediction Model Example
N[S] = 100.000
N[F] = 500
N[A] = 2500
N[E] = 100
P(F) = N[F]/N[S] -> 500/100.000 = 0,005
P(A\F) = N[E]/N[S] -> 100/500 = 0.2
P (A) = N[A]/N[S] -> 2500/100.000 = 0.025
CondP(F\A) = ((N[F]/N[S]) · (N[E]/N[S])) / (N[A]/N[S]) -> 0,005 × 0.2/0.025 = 0.04
Given the presence of forest type A, the probability of a forest fire occurrence is 8 times greater than the prior probability
26-10-2003 7
Likelihood Ratio Prediction Model
Represents the ratio of two spatial distribution functions: one with forest fire and one without occurrences
LR(A\F) = P(A\F)
P (A\F)
LR(A\F) = N[E] · (N[S] – N[F])
N[F] · (N[A] – N[E])
N[E] · (N[S] – N[F]) = 100 * (100.000 - 500) = 9.950.000
N[F] · (N[A] – N[E]) = 500 * (2500 - 100) = 1.200.000
LR(A\F) = 9.950.000/1.200.000 = 8,2916
>1: positive evidence for forest ignition
1: uncorrelated
<1: negatively correlated
26-10-2003 8
Prediction Procedure
-Forestry Maps
-Topography Maps
-Human Activities
-Fire History Data
-A large number of thematic layers can be suitable related to forest fire occurrences
-Relevance filter is subjective
-> Thematic layers are user-selected
26-10-2003 9
Forest Fire Hazard Rate
Multiple Layer integration shares intermediate analysis with other levels
FHR: Forest Fire Hazard Rate:
FHR(p)CondP = CondP(V1(p)) ×…× CondP(Vm(p)), i=1,…,m
FHR(p)LR = LR(V1(p)) ×…× LR(Vm(p)), i=1,…,m
Vi(p) = Attribute value at the point thematic map (i)
CondP = Conditional Probability
LR = Likelihood Ratio
For each local area, a FHR can be computed, and fire ignition danger can be analysed
26-10-2003 10
Experiment: Attribute selection
For practical use, thematic layers must be selected, based on relative importance for explaining fire ignition
Condition: chosen layers have to be conditionally independent
Layers for Experiment:
-Forest Type
-Elevation
-Slope
-Road Network
-Farms
-Building Boundaries
26-10-2003 11
Experiment: Data sets
-It is assumed the time of study was 1996:
All spatial data in 1996 are compiled, including distribution of fire ignition locations which occurred prior to that year
-Cross Validation: Predictions based on those relationships are evaluated by comparing the estimated hazard classes with the distribution of forest fire ignition locations that occurred after 1996, during the period 1997 to 2001
- Evaluation of Conditional Probability and Likelihood Ratio can expressed in a Prediction Rate Curve
26-10-2003 12
Expiriment: Evaluation
Prediction Rates with respect to the ‘future’ 1997 to 2001 forest fire occurrences
Prediction rate curve of both models
The effectiveness of the model estimated are acceptable
Conclusion:Likelihood Ratio is a more powerful method than Conditional Probability.
26-10-2003 13
Expiriment: Visualisation
Using Forest Fire Hazard Index (FHI)
-Sort estimated probabilities of all pixels in descending order
-ordered pixels are divided into 11 classes:
Pixels with the highest 5% estimated probability are classified as the first class, the next 5% as second class and so on.
-Remaining low 50% is assigned to the last class
-Add color to classes
26-10-2003 14
Conclusion
The Likelihood ratio method is more powerful than the Conditional probability method.
Prediction of the forest fire hazardous area could be helpful to increase the efficiency of forest fire management:
The ability to quantify the ignition risk could lead to a more informed allocation of fire prevention resources.
Statistics based Forest Fire prediction works well.
26-10-2003 15
Questions