Integrating explainable AI and multi-objective optimization for wildfire resource allocation under fixed staffing constraints: a decision-oriented framework

Abstract Emergency response agencies operating under fixed staffing constraints face a structural challenge: translating spatially heterogeneous wildfire risk into operationally feasible allocation decisions. This study presents a decision-support framework integrating explainable artificial intelligence (XAI) and multi-objective optimization for wildfire resource allocation. Using 544 wildfire events from Taiwan (2011–2024), an Extreme Gradient Boosting (XGBoost) classifier predicts fire severity risk, achieving a test-set ROC-AUC of 0.810 and F1-score of 0.691. Shapley Additive Explanations (SHAP) analysis identifies spatial factors (fire station, township, coordinates) and temporal factors (month, year) as dominant predictors, with forest type and meteorological variables contributing secondary roles. The predicted risk distribution is embedded into a tri-objective model minimizing a response-time proxy (a distance-weighted resource concentration index capturing nonlinear response efficiency), workload imbalance, and a burned-area proxy (an empirically calibrated power-law function linking response delay to nonlinear fire spread escalation), solved via the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to generate Pareto-optimal solutions under fixed staffing constraints. Balanced allocation—deploying 60–63% of resources to high-risk zones—achieves a response-time proxy of 1.88 (27.7% reduction vs. uniform deployment), while maintaining workload imbalance at 0.18. Efficiency-oriented strategies reduce response time to 1.83 but increase workload concentration by 28%. The Pareto frontier makes efficiency–equity trade-offs explicit and supports reproducible pre-positioning decisions. Although parameterized using Taiwan wildfire data, the prediction–interpretability–optimization chain imposes no region-specific assumptions and can be recalibrated to any context with comparable institutional constraints.