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    白海峰, 刘晓东, 牛树奎, 何亚东. 基于贝叶斯模型平均法的森林火灾预测模型构建研究以云南省大理州为例[J]. 北京林业大学学报, 2021, 43(5): 44-52. DOI: 10.12171/j.1000-1522.20200173
    引用本文: 白海峰, 刘晓东, 牛树奎, 何亚东. 基于贝叶斯模型平均法的森林火灾预测模型构建研究以云南省大理州为例[J]. 北京林业大学学报, 2021, 43(5): 44-52. DOI: 10.12171/j.1000-1522.20200173
    Bai Haifeng, Liu Xiaodong, Niu Shukui, He Yadong. Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example[J]. Journal of Beijing Forestry University, 2021, 43(5): 44-52. DOI: 10.12171/j.1000-1522.20200173
    Citation: Bai Haifeng, Liu Xiaodong, Niu Shukui, He Yadong. Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example[J]. Journal of Beijing Forestry University, 2021, 43(5): 44-52. DOI: 10.12171/j.1000-1522.20200173

    基于贝叶斯模型平均法的森林火灾预测模型构建研究以云南省大理州为例

    Construction of forest fire prediction model based on Bayesian model averaging method: taking Dali Prefecture, Yunnan Province of southwestern China as an example

    • 摘要:
        目的  本文基于贝叶斯模型平均法,结合二项逻辑斯蒂回归模型,构建云南省大理州森林火灾发生预测模型,以期提高林火预测精度,为研究地区林火管理提供技术支持。
        方法  利用2000—2013年大理州林火数据及对应的气象数据,分别运用二项逻辑斯蒂回归模型和贝叶斯模型平均法,对该地区森林火灾对气象因子的响应进行实证分析。二项逻辑斯蒂回归模型为单一模型,建模前通过对各解释变量进行多重共线性检验,剔除有显著共线性的解释变量,然后通过逐步回归法,筛选最终变量并进行参数拟合。贝叶斯平均模型为组合模型,基于贝叶斯模型平均法建模时,采用奥卡姆窗的方法来适当调整模型空间,并以5个最优模型的后验概率作为权重进行加权建模。将全样本数据随机分成80%的训练样本和20%的测试样本,基于训练样本建立模型,对测试样本进行预测,通过对比观测值和预测值计算模型的准确率。
        结果  通过二项逻辑斯蒂模型拟合,优度为0.783,预测精度为0.718。通过贝叶斯平均模型拟合,优度为0.868,预测精度为0.807。2个模型预测结果对比显示,在训练集中,贝叶斯平均模型的预测准确率比二项逻辑斯蒂回归模型高9.3%;在测试集中,贝叶斯平均模型的预测准确率比二项逻辑斯蒂回归模型高8.9%。
        结论  在基于气象因子的大理州林火发生预测模型构建研究中,贝叶斯平均模型的拟合优度和预测精度均高于二项逻辑斯蒂模型,表明贝叶斯模型平均法具有一定的现实应用意义,可用于提高研究地区林火预测精度,有利于森林火灾的决策管理。

       

      Abstract:
        Objective  Based on the Bayesian model averaging method and binomial Logistic regression model, this paper constructs a forest fire prediction model in Dali Prefecture, Yunnan Province of southwestern China, so as to improve the prediction accuracy of forest fire and provide technical support for forest fire management in the study area.
        Method  Using the forest fire data and corresponding meteorological data of Dali Prefecture from 2000 to 2013, the binomial Logistic regression model and the Bayesian model averaging method were used to empirically analyze the response of forest fires to meteorological factors in this area. The binomial Logistic regression model is a single model. Before modeling, the explanatory variables with significant collinearity were eliminated by multicollinearity test. Then, the final variables were screened by stepwise regression method and the parameters were fitted. The Bayesian average model is a combined model. When modeling based on the Bayesian model averaging method, the Occam’s window method was used to appropriately adjust the model space, and the posterior probabilities of the five optimal models were used as weights for weighted modeling. In this paper, the all sample data were randomly divided into 80% training samples and 20% test samples. A model was built based on the training samples to predict the test samples. The accuracy of the model was calculated by comparing the observations and predictions.
        Result  Fitting through the binomial Logistic model, the results showed that: the model fitting goodness was 0.783, and the prediction accuracy was 0.718; through the Bayesian average model fitting, the results showed that: the model fitting goodness was 0.868, and the prediction accuracy was 0.807. The comparison of the prediction results of the two models showed that: in the training set, the prediction accuracy of the Bayesian average model was 9.3% higher than that of the binomial Logistic regression model; and in the test set, the former was 8.9% higher than the latter.
        Conclusion  In the prediction model of forest fire occurrence in Dali Prefecture based on meteorological factors, the goodness of fit and prediction accuracy of Bayesian average model were higher than that of binomial Logistic model, indicating that the Bayesian model averaging method had certain practical application significance. It can be used to improve the prediction accuracy of forest fire in the study area, which is beneficial to the decision management of forest fire.

       

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