Studying the Impact of Stochasticity on the Evaluation of Deep Neural Networks for Forest-Fire Prediction

arXiv:2402.15163v1 Announce Type: new
Abstract: 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 …

abstract arxiv assumptions cs.ai cs.lg evaluation fire focus framework impact networks neural networks paper prediction stochastic study studying systems type wildfire