1 / 15

Jong Gyu Han, Keun Ho Ryu, Kwang Hoon Chi and Yeon Kwang Yeon

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

meagan
Télécharger la présentation

Jong Gyu Han, Keun Ho Ryu, Kwang Hoon Chi and Yeon Kwang Yeon

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

  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

  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

  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

  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]

  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

  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

  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

  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

  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

  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

  12. 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

  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

  14. 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.

  15. Questions

More Related