Abstract: [Objective] In this study, two methods were applied to establish fire prediction model for Tahe, Daxing'an Mountains. Our objective is to identify the applicability of random forest algorithm to local forest fire prediction according to prediction accuracy comparison. This study would provide some technical support for local forest fire management. [Method] The fire data collected in Tahe, Daxing'an Mountains between 1974 and 2008 were used in a case study to identify the relationship between fire occurrence and meteorological factors by using logistic regression (LR) model and random forest (RF) algorithm, respectively. In order to reduce the influence of sample distribution on the model fitting, the original dataset was randomly divided into training (60%) and validation (40%) samples. The procedure was repeated five times applying a sampling with replacement method, thus obtaining five random sub-samples (sample groups) of the data, each with a training and validation dataset. The predictors that had been proved to be significant at ɑ=0.05 in at least three of five intermediate models were included in the final models. Besides, in the present study a "cross validation" test was to identify the accuracy of the two models. [Result] The results of model parameter estimation indicated that daily minimum relative humidity, fine fuel moisture content (FFMC) and drought code (DC) were identified as important predictors in both Logistic and Random Forest model. The result of model fitting revealed that the prediction accuracy of LR model in five intermediate models were 8% and 10% lower than that of RF,respectively, for the training and variation samples. However, the prediction accuracy of RF on the complete dataset was 15% higher than that of LR. In the Cross Validation test, the prediction accuracy of RF was 85.0%, higher than that of LR (76.2%) and the result agreed with that of five sample groups. [Conclusion] Our results revealed that the RF model was superior to LR model on the fire prediction in the study area, thus the RF model can be used in the fire prediction and provide important information for the local fire management and plan.

Key words: Tahe area, fire occurrence, meteorological factors, random forest algorithm, Logistic regression