Spatial predictions of wildfire spread are used operationally and in risk estimation. It is important that their outputs are validated to quantify predictive performance and uncertainty. There are numerous loss functions for this simulation validation. Our paper synthesises ten common and five novel loss functions for evaluating the performance of wildfire predictions against observed or simulated wildfires. We describe each loss function; test their sensitivity to scale, rotation and translation; and demonstrate their application with three case study wildfires in south-eastern Australia. Based on their purpose, there are three categories of loss functions: general overlap, partial overlap and distance. Within these categories, there is functional redundancy as many loss functions are appropriate and perform similarly. Using loss functions from across the three different categories is the most comprehensive for evaluating wildfire models, but the choice depends on the simulation purpose and interpretation.

野火蔓延的空间预测可用于操作和风险评估。重要的是，他们的输出经过验证以量化预测性能和不确定性。有许多损失函数可用于此模拟验证。我们的论文综合了十种常见的损失函数和五种新颖的损失函数，用于评估野火预测对观察或模拟野火的性能。我们描述每个损失函数；测试他们对缩放、旋转和平移的敏感度；并通过澳大利亚东南部的三个野火案例研究来展示其应用。根据其目的，损失函数分为三类：一般重叠、部分重叠和距离。在这些类别中，存在功能冗余，因为许多损失函数都是合适的并且表现相似。使用三个不同类别的损失函数是评估野火模型最全面的方法，但选择取决于模拟目的和解释。