Statistics Based Predictive Geo-Spatial Data Mining: Forest Fire Hazardous Area Mapping Application

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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

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### 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
• Predicting forest fire hazardous areas from large spatial data sets
• Leads to a forest fire hazard prediction model

### Problem Definition 2

Youngdong Region of Kangwan Province, Republic of Korea

Using:

-Historical data on fire ignition point locations

-Grid-based multi-layer GIS

### 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

### 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]

### 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

### 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

### 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

### 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

### 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
• Farms
• Building Boundaries

### 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

### Expiriment: Evaluation

Prediction rate curve of both models

Conclusion:

Likelihood Ratio is a more powerful method than Conditional Probability.

The effectiveness of the model estimated are acceptable

Prediction Rates with respect to the ‘future’ 1997 to 2001 forest fire occurrences

### 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

### Conclusion

Statistics based Forest Fire prediction works well.

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.