基于自回归条件生成对抗网络的概率野火传播预测
Probabilistic Wildfire Spread Prediction Using an Autoregressive Conditional Generative Adversarial Network
AI总结：
本文提出基于自回归条件生成对抗网络的野火传播预测方法，通过对抗学习提升预测准确性和物理可解释性，以应对野火的复杂非线性动态。
摘要：
气候变化加剧了野火的频率和严重性，使快速准确预测火势蔓延变得至关重要。基于物理的模拟器如FARSITE提供高保真预测，但计算成本高，限制了实时决策的应用。现有深度学习模型往往产生过于平滑的预测，无法捕捉野火传播的复杂非线性动态。本文提出了一种自回归条件生成对抗网络（CGAN）用于概率野火传播预测。通过将预测任务 formulation 为自回归问题，模型学习序列状态转换，确保长期预测的稳定性。实验结果表明，所提出的基于CGAN的模型在整体预测准确性和火场边界界定方面均优于传统深度学习模型。这些结果表明，对抗学习使模型能够捕捉野火传播的强非线性和不确定性，而非仅拟合像素平均值。此外，自回归框架促进了野火演化的系统时间预测。所提出的基于CGAN的自回归框架提高了野火传播预测的准确性和物理可解释性，为时间敏感的响应和疏散规划提供了有前景的基础。
英文摘要：
Climate change has intensified the frequency and severity of wildfires, making rapid and accurate prediction of fire spread essential for effective mitigation and response. Physics-based simulators such as FARSITE offer high-fidelity predictions but are computationally intensive, limiting their applicability in real-time decision-making, while existing deep learning models often yield overly smooth predictions that fail to capture the complex, nonlinear dynamics of wildfire propagation. This study proposes an autoregressive conditional generative adversarial network (CGAN) for probabilistic wildfire spread prediction. By formulating the prediction task as an autoregressive problem, the model learns sequential state transitions, ensuring long-term prediction stability. Experimental results demonstrate that the proposed CGAN-based model outperforms conventional deep learning models in both overall predictive accuracy and boundary delineation of fire perimeters. These results demonstrate that adversarial learning allows the model to capture the strong nonlinearity and uncertainty of wildfire spread, instead of simply fitting the pixel average. Furthermore, the autoregressive framework facilitates systematic temporal forecasting of wildfire evolution. The proposed CGAN-based autoregressive framework enhances both the accuracy and physical interpretability of wildfire spread prediction, offering a promising foundation for time-sensitive response and evacuation planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
