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传统的气象林火火险预测方法只适应于省级以上的大地域林火发生风险的宏观预测,不能用于林场一级的小地域的林火发生预测。林场一级的单位缺乏精确的林火发生数据和实用有效的林火监测手段,记录数据一般较粗放,难以用精确的方法进行预测。本文利用关联算法Apriori,分析了北京市房山区林火发生的可能性及发生强度与气象因子间的关系。研究结果表明,Apriori算法可以有效地利用粗放的林火数据集进行林火预测,为基层林场预测林火发生提供了方法。
The traditional method of forecasting forest fires based on fire is only suitable for macro-forecasting the risk of forest fires above the provincial level and can not be used to predict the occurrence of forest fires in small-scale forest areas. The unit at the forest farm lacks accurate data on the occurrence of forest fires and practical and effective forest fire monitoring methods. The recorded data are generally extensive and difficult to predict with accurate methods. In this paper, the correlation algorithm Apriori is used to analyze the possibility of occurrence of forest fires in Fangshan District, Beijing and the relationship between intensity and meteorological factors. The results show that Apriori algorithm can effectively make use of extensive forest fire data sets to predict forest fires and provide a method for predicting forest fire occurrence in grass-roots forest farms.