Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering
Ali Akbar Jafarzadeh, Ali Mahdavi, Heydar Jafarzadehhttps://doi.org/10.17221/7/2017-JFSCitation:Jafarzadeh A.A., Mahdavi A., Jafarzadeh H. (2017): Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering. J. For. Sci., 63: 370-380.
In this study we evaluated forest fire risk in the west of Iran using the Apriori algorithm and fuzzy c-means (FCM) clustering. We used twelve different input parameters to model fire risk in Ilam Province. Our results with minimum support and minimum confidence show strong relationships between wildfire occurrence and eight variables (distance from settlement, population density, distance from road, slope, standing dead oak trees, temperature, land cover and distance from farm land). In this study, we defined three clusters for each variable: low, middle and high. The data regarding the factors affecting forest fire risk were distributed in these three clusters with different degrees of membership and the final map of all factors was classified by FCM clustering. Each layer was then created in a geographic information system. Finally, wildfire risks in the area obtained from overlaying these layers were classified into five categories, from very low to very high according to the degree of danger.Keywords:
wildfire; association rules; fuzzification; Ilam Province; IranReferences:
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