Prediction of Forest Fire Hazardous Area Using Predictive Spatial Data Mining 


Vol. 9,  No. 6, pp. 1119-1126, Dec.  2002
10.3745/KIPSTD.2002.9.6.1119


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  Abstract

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending on the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is more powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

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  Cite this article

[IEEE Style]

J. G. Han, Y. K. Yeon, K. H. Chi, K. H. Ryu, "Prediction of Forest Fire Hazardous Area Using Predictive Spatial Data Mining," The KIPS Transactions:PartD, vol. 9, no. 6, pp. 1119-1126, 2002. DOI: 10.3745/KIPSTD.2002.9.6.1119.

[ACM Style]

Jong Gyu Han, Yeon Kwang Yeon, Kwang Hoon Chi, and Keun Ho Ryu. 2002. Prediction of Forest Fire Hazardous Area Using Predictive Spatial Data Mining. The KIPS Transactions:PartD, 9, 6, (2002), 1119-1126. DOI: 10.3745/KIPSTD.2002.9.6.1119.

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