Wildfires threaten ecosystems, economies, and public health, particularly in high-risk regions. Accurate wildfire prediction remains challenging due to complex interactions among weather patterns, vegetation dynamics, climate change, and human activities. This study investigates the role of advanced metaheuristic algorithms in optimizing feature selection for wildfire prediction across eight Canadian provinces, focusing on improving accuracy and computational efficiency. We evaluate twelve algorithms, including Atom Search Optimization (ASO), Barnacles Mating Optimizer (BMO), Chef-Based Optimization (CBO), Energy Valley Optimizer (EVO), Equilibrium Optimizer (EO), and Walrus Optimization Algorithm (WOA), among others.
Key results highlight the superior performance of BMO and EVO, with BMO achieving average recall rates of 77.55% in Alberta and 76.51% in Quebec, and EVO attaining 76.96% and 78.30% in these provinces, respectively. In contrast, ASO consistently underperformed, yielding recall rates as low as 44.67% in Ontario and 51.99% in the Northwest Territories. Statistical analyses using Friedman and Wilcoxon signed-rank tests confirmed significant differences in algorithmic performance (p < 0.05), with spiral-enhanced variants of the Liver Cancer Algorithm (LCA) outperforming the baseline LCA. Furthermore, Random Forest and Gradient Boosting emerged as the most reliable prediction models, emphasizing the synergy between optimized feature selection and robust machine learning frameworks.
A significant contribution of this research is the enhancement of the LCA through spiral updates, specifically the Euler Spiral, which improves the balance between exploration and exploitation in the search space. This enhancement addresses the instability and slow convergence often associated with the standard LCA. Although the spiral updates improved LCA’s performance, algorithms like EVO and the Genetic Algorithm (GA) consistently outperformed LCA in recall and overall predictive accuracy across provinces.
The findings of this study highlight the variability in algorithm performance and emphasize the importance of tailoring wildfire prediction strategies to specific regional conditions. This work not only evaluates the capabilities of advanced metaheuristic algorithms but also identifies key environmental factors that predict wildfire risk, offering practical insights to enhance wildfire risk management and mitigation efforts.
