Shapley-based interpretation of deep learning models for wildfire spread rate prediction

Abstract

Predicting wildfire progression is vital for countering its detrimental effects. While numerous studies over the years have delved into forecasting various elements of wildfires, many of these complex models are perceived as “black boxes”, making it challenging to produce transparent and easily interpretable outputs. Evaluating such models necessitates a thorough understanding of multiple pivotal factors that influence their performance. This study introduces a deep learning methodology based on transformer to determine wildfire susceptibility. To elucidate the connection between predictor variables and the model across diverse parameters, we employ SHapley Additive exPlanations (SHAP) for a detailed analysis. The model’s predictive robustness is further bolstered through various cross-validation techniques. Upon examining various wildfire spread rate prediction models, transformer stands out, outperforming its peers in terms of accuracy and reliability. Although the models demonstrated a high level of accuracy when applied to the development dataset, their performance deteriorated when evaluated against the separate evaluation dataset. Interestingly, certain models that showed the lowest errors during the development stage exhibited the highest errors in the subsequent evaluation phase. In addition, SHAP outcomes underscore the invaluable role of explainable AI in enriching our comprehension of wildfire spread rate prediction.