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首页> 外文期刊>International Journal of Disaster Risk Science >Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China
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Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China

机译：基于卷积神经网络的中国云南森林火灾敏感性分析

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Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures—Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.
机译：森林大火给全世界的生态，社会和经济造成了巨大损失。为了最大程度地减少这些损失并减少森林火灾，对森林火灾进行建模和预测是有意义的，因为它们可以支持森林火灾的预防和管理。近年来，卷积神经网络（CNN）已成为重要的最新深度学习算法，其实现丰富了许多领域。因此，我们提出了使用CNN的森林火灾敏感性空间预测模型。使用地理信息系统绘制了2002年至2010年中国云南省过去的森林火灾地点，以及一组14种森林火灾影响因素。应用过采样来消除类不平衡，并使用比例分层采样来构建训练/验证样本库。设计了适合于森林火灾敏感性预测的CNN体​​系结构，并优化了超参数以提高预测精度。然后，将测试数据集输入经过训练的模型中，以构建云南省森林火灾敏感性的空间预测图。最后，使用几种统计方法（Wilcoxon符号秩检验，接收器工作特性曲线和曲线下面积（AUC））评估了所提出模型的预测性能。结果证实了所提出的CNN模型（AUC 0.86）的准确性高于随机森林，支持向量机，多层感知器神经网络和核对数回归基准分类器。 CNN具有更强的拟合和分类能力，可以充分利用邻域信息，是森林火灾敏感性空间预测的有前途的替代方法。这项研究将CNN的应用扩展到森林火灾敏感性的预测中。

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