Background: Understanding the intricacies of wildfire impact across diverse geographical landscapes necessitates a nuanced comprehension of fire dynamics and areas of vulnerability, particularly in regions prone to high wildfire risks. Machine learning (ML) stands as a formidable ally in addressing the complexities associated with predicting and mapping these risks, offering advanced analytical capabilities. Nevertheless, the reliability of such ML approaches is heavily contingent on the integrity of data and the robustness of training protocols. The scientific community has raised concerns about the transparency and interpretability of ML models in the context of wildfire management, recognizing the need for these models to be both accurate and understandable. The often-opaque nature of complex ML algorithms can obscure the rationale behind their outputs, making it imperative to prioritize clarity and interpretability to ensure that model predictions are not only precise but also actionable. Furthermore, a thorough evaluation of model performance must account for multiple critical factors to ensure the utility and dependability of the results in practical wildfire suppression and management strategies.
Results: This study unveils a sophisticated spatial deep learning framework grounded in TabNet technology, tailored specifically for delineating areas susceptible to wildfires. To elucidate the predictive interplay between the model’s outputs and the contributing variables across a spectrum of inputs, we embark on an exhaustive analysis using SHapley Additive exPlanations (SHAP). This approach affords a granular understanding of how individual features sway the model’s predictions. Furthermore, the robustness of the predictive model is rigorously validated through 5-fold cross-validation techniques, ensuring the dependability of the findings. The research meticulously investigates the spatial heterogeneity of wildfire susceptibility within the designated study locale, unearthing pivotal insights into the nuanced fabric of fire risk that is distinctly local in nature.
Conclusion: Utilizing SHapley Additive exPlanations (SHAP) visualizations, this research meticulously identifies key variables, quantifies their importance, and demystifies the decision-making mechanics of the model. Critical factors, including temperature, elevation, the Normalized Difference Vegetation Index (NDVI), aspect, and wind speed, are discerned to have significant sway over the predictions of wildfire susceptibility. The findings of this study accentuate the criticality of transparency in modeling, which facilitates a deeper understanding of wildfire risk factors. By shedding light on the significant predictors within the models, this work enhances our ability to interpret complex predictive models and drives forward the field of wildfire risk management, ultimately contributing to the development of more effective prevention and mitigation strategies.