基于气象因子的黑龙江黑河林火发生概率预测.

Based on the Logistic regression model, this study analyzed the relationship between the occurrence of forest fires and meteorological factors, determined the main meteorological factors, and established the prediction model for Heihe region in Heilongjiang Province. Historical forest fire and meteorological data from 1981 to 2015 in Heihe region were randomly divided into 80% building data and 20% validation data. R software was used to establish five sub-models and screen out significant meteorological factors by Logistic stepwise regression (P＜0.05) . Moreover, a forest fire prediction model was established using the Logistic regression model to analyze the main meteorological factors. The results showed that daily maximum wind speed, daily maximum temperature, Daily average water vapor pressure, daily average relative humidity, and daily precipitation are the significant meteorological factors affecting the occurrence of forest fires (P＜0.05) . The former two meteorological factors are positively correlated with forest fire occurrence, whereas the latter three factors are negatively correlated with such occurrence. The prediction accuracy of the Logistic regression model is 82.83%～86.12%. Area under the curve ranges from 0.884 to 0.920. The Logistic regression model has good prediction accuracy and high goodness of fit, which is suitable for forest fire occurrence probability prediction research based on meteorological factors in Heihe region. [ABSTRACT FROM AUTHOR]