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.

翻译：本文首次系统地研究了在随机性假设下，深度神经网络用于离散动力系统的评估问题，重点关注野火预测。我们构建了一个框架，用于分析随机性对两类评估指标的影响：基于分类的指标（评估与观测真实值的一致性）和恰当评分规则（检验统计一致性）。研究发现，在高随机性场景中，统计一致性评估是可靠的替代方案。我们将分析扩展到真实野火数据，揭示了传统野火预测评估方法的局限性，并提出了可解释且适应随机性的替代方案。