对比五种深度学习模型，评估其预测野火蔓延效果
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
- 融合气象与环境变量的ConvLSTM模型表现最佳
- 深度学习模型灵活性高，但FARSITE精度更高
- 结合可解释AI识别出2023毛伊岛火灾关键因素
准确预测野火蔓延对有效防火管理和风险评估至关重要。随着人工智能快速发展，多种深度学习模型被用于野火蔓延预测，但对其优劣及与传统非AI模型的对比仍缺乏深入理解。本文基于夏威夷十年以上的野火数据，评估了五种典型深度学习模型在融合气象与环境变量下的预测能力，并以2023年毛伊岛火灾为案例，将表现最佳的AI模型与广泛应用的FARSITE模型进行对比。结果表明，两种深度学习模型——ConvLSTM及其带注意力机制的变体，在五种模型中表现最优。相较之下，FARSITE具有更高精确率、更低召回率和更高F1分数；而AI模型则展现出更强的输入数据适应性。通过引入可解释人工智能方法，进一步识别出影响2023年毛伊岛火灾的关键气象与环境因子。
原文摘要 · Abstract (English)
Predicting the spread of wildfires is essential for effective fire management and risk assessment. With the fast advancements of artificial intelligence (AI), various deep learning models have been developed and utilized for wildfire spread prediction. However, there is limited understanding of the advantages and limitations of these models, and it is also unclear how deep learning-based fire spread models can be compared with existing non-AI fire models. In this work, we assess the ability of five typical deep learning models integrated with weather and environmental variables for wildfire spread prediction based on over ten years of wildfire data in the state of Hawaii. We further use the 2023 Maui fires as a case study to compare the best deep learning models with a widely-used fire spread model, FARSITE. The results show that two deep learning models, i.e., ConvLSTM and ConvLSTM with attention, perform the best among the five tested AI models. FARSITE shows higher precision, lower recall, and higher F1-score than the best AI models, while the AI models offer higher flexibility for the input data. By integrating AI models with an explainable AI method, we further identify important weather and environmental factors associated with the 2023 Maui wildfires.
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