This paper presents the first systematic study of the evaluation of Deep Neural Networks (DNNs) for discrete dynamical systems under stochastic assumptions, with a focus on wildfire prediction. We develop a framework to study the impact of stochasticity on two classes of evaluation metrics: classification-based metrics, which assess fidelity to observed ground truth (GT), and proper scoring rules, which test fidelity-to-statistic. Our findings reveal that evaluating for fidelity-to-statistic is a reliable alternative in highly stochastic scenarios. We extend our analysis to real-world wildfire data, highlighting limitations in traditional wildfire prediction evaluation methods, and suggest interpretable stochasticity-compatible alternatives.

本文首次系统研究了在随机假设下对离散动力系统的深度神经网络（DNN）进行评估，并重点关注野火预测。我们开发了一个框架来研究随机性对两类评估指标的影响：基于分类的指标，用于评估与观测地面真实性（GT）的一致性；和正确性评分规则，用于测试与统计数据一致性。我们的研究结果表明，在高度随机的情况下，评估与统计数据一致性是一个可靠的替代方法。我们将分析扩展到现实世界的野火数据中，突出传统野火预测评估方法的局限性，并提出了可解释的适用于随机性的替代方法。

研究随机性对深度神经网络进行森林火灾预测评估的影响