Learning a Bayesian network model for predicting wildfire behavior
Learning a Bayesian network model for predicting wildfire behavior
复制标题
学习用于预测野火行为的贝叶斯网络模型
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
D. Štraub
中科院分区：
文献类型：
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作者：
Kilian Zwirglmaier;P. Papakosta;D. Štraub
开发了用于预测野火蔓延的贝叶斯网络（BN）模型。在自动结构学习算法的帮助下，从与天气、地形和土地覆盖相关的可用指标变量中选择了信息最丰富的变量。然后使用现象学推理根据这些指标构建最终的 BN 模型。完整模型的自动结构学习
由于大量的变量与有限的数量相结合，我们发现该方法具有严重的局限性。
观察。 BN 模型是利用地中海塞浦路斯岛的数据进行学习和验证的。
最终的 BN 与作为基准的朴素贝叶斯分类器 (NBC) 进行了比较，结果是
表明适用于预测目的。
A Bayesian network (BN) model for predicting wildfire spreading was developed. From the available indicator variables related to weather, topography and land cover, the most informative were selected with the help of automatic structure learning algorithms. A final BN model was then constructed from these indicators using phenomenological reasoning. Automatic structure learning of the complete model
was found to have severe limitations due to large number of variables in combination with limited number of
observations. The BN model was learned and validated with data from the Mediterranean island of Cyprus.
The final BN was compared to a Naive Bayesian Classifier (NBC), which serves as a benchmark, and it was
shown to be applicable for prediction purposes.