论文标题
火阵线传播的仿真框架
An Emulation Framework for Fire Front Spread
论文作者
论文摘要
预测丛林大火传播是预防火灾和响应工作中的重要因素。丛林大火传播的经验观察可用于在某些条件下估算火灾反应。这些观察结果构成了扩展模型，可用于生成模拟。我们使用机器学习来推动灌木丛的仿真方法，并表明仿真有能力密切复制模拟的消防数据。我们提出了一种初步的仿真方法，具有快速模拟复杂模拟的能力。然后可以作为集合估计技术的一部分生成大量预测，该技术提供了更强大，更可靠的随机系统预测。
Forecasting bushfire spread is an important element in fire prevention and response efforts. Empirical observations of bushfire spread can be used to estimate fire response under certain conditions. These observations form rate-of-spread models, which can be used to generate simulations. We use machine learning to drive the emulation approach for bushfires and show that emulation has the capacity to closely reproduce simulated fire-front data. We present a preliminary emulator approach with the capacity for fast emulation of complex simulations. Large numbers of predictions can then be generated as part of ensemble estimation techniques, which provide more robust and reliable forecasts of stochastic systems.
