Predicting the Wildland Fire Spread Using a Mixed-Input CNN Model with Both Channel and Spatial Attention Mechanisms

The prediction of wildfire spreading is necessary for managing and fighting the forest fire. The traditional models require higher accuracy of the input parameters, which is impossible in real forest fires. The paper proposed a fire-spreading model based on the dynamic data of the fire field to improve its adaptability. The model is designed using a convolutional neural network with mixed-inputs and attention mechanisms (MI-AM-CNN). It predicts the burn map after a period of time through the multiple-channel image containing terrain variables and the current burn map, and the scalars containing climate variables. The channel and spatial attention modules are integrated to handle the advanced features that contain important fire variables information and strengthen the influence of important features on the prediction. Based on the FARSITE, a large number of data sets are generated for training, validating, and testing the models in the paper. The proposed model MI-AM-CNN is compared with the state-of-the-art neural network models. Quantitative results show that MI-AM-CNN has the highest performance in predicting effectiveness and efficiency, and it can be applied recursively to get the continuous predicted results. In addition, the prediction results of MI-AM-CNN on the historical fire data demonstrate the ability of its application in the real fire case. The MI-AM-CNN can be used as a predictive method in firefighting operations, and its predicted results can provide theoretical support for the forest fire spread prediction method based on artificial intelligence.