Guoli Zhang, Ming Wang, Kai Liu. Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China[J]. International Journal of Disaster Risk Science, 2019, 10(3): 386-403. doi: 10.1007/s13753-019-00233-1
Citation: Guoli Zhang, Ming Wang, Kai Liu. Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China[J]. International Journal of Disaster Risk Science, 2019, 10(3): 386-403. doi: 10.1007/s13753-019-00233-1

Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China

doi: 10.1007/s13753-019-00233-1

This research was supported by the National Key Research and Development Plan (2017YFC1502902) and National Natural Science Foundation of China (41621601). The financial support is highly appreciated. We thank Yinxue Cao for her help in getting the data from Climate Forecast System Reanalysis. We are also grateful to the anonymous reviewers and the editors for their constructive comments.

  • Available Online: 2021-04-26
  • 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.
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