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

The prediction of forest fires through Deep Neural Networks (DNNs) presents a critical tool in mitigating the devastating effects of wildfires. However, the inherent stochastic nature of such environmental phenomena poses significant challenges in accurately evaluating these predictive models. This paper introduces a pioneering framework tailored to assess the impact of stochasticity on the performance of DNNs specifically designed for wildfire prediction.

Distinguishing between two primary categories of evaluation metrics—classification-based metrics and proper scoring rules—our analysis sheds light on the effectiveness of each in scenarios marked by high levels of uncertainty. The study’s key revelation is the superior reliability of fidelity-to-statistic evaluations over traditional fidelity-to-observed ground truth (GT) metrics in contexts where stochastic elements are predominant.

Extending the framework to actual wildfire incidents, our findings critically evaluate the limitations inherent in traditional model assessment techniques. In response, we propose novel, interpretable alternatives that are compatible with the stochastic nature of wildfire prediction. These alternatives not only enhance the accuracy of model evaluations but also provide clearer insights into the models’ predictive capabilities under varying conditions.

Through this systematic exploration, our work not only advances the field of environmental modeling and prediction but also offers practical guidelines for the development and assessment of more robust, reliable DNN-based prediction systems for forest fires and other stochastic environmental challenges.

Recommended citation: Harshit Kumar, Biswadeep Chakraborty, Beomseok Kang, Saibal Mukhopadhyay. (2024). "Studying the Impact of Stochasticity on the Evaluation of Deep Neural Networks for Forest-Fire Prediction." arXiv preprint arXiv:2402.15163.