A Scope-Aware and Explainable AI Decision-Support System for Municipal Waste-Collection Dispatch in Smart City Operations

Predictive performance in operational AI depends on deployment conditions, whereas model explanations alone do not determine feasible actions. This study developed and evaluated a scope-aware and explainable AI decision-support system for municipal waste collection dispatch in Yonghe District, Taiwan. A retrospective evaluation included 11,934 vehicle-day observations from 17 vehicles, and a six-month prospective deployment recorded 1989 vehicle-days. Ten classifiers were benchmarked using development-period data only. The selected XGBoost model achieved full-history weighted F1 values of 0.9261 in established-unit chronological evaluation and 0.913 under leave-one-vehicle-out (LOVO) validation; reduced-history values were 0.893 and 0.861, respectively. Class-specific diagnostics showed a red-state recall of 0.9358 chronologically and 0.9144 in pooled LOVO predictions. Under the same LOVO design, GBDT, LightGBM, and Random Forest achieved weighted F1 values of 0.901, 0.896, and 0.887, respectively. During prospective use, 143 of 209 evaluated red cases (68.4%) were scenario sensitive, and 94.6% of scenario evaluations were completed within the 30–60-min decision window. Because supervisors could act after viewing model outputs, prospective records were interpreted as workflow evidence rather than independent predictive validation. The performance envelope links each performance claim to the evaluated population, information availability, operational context, and validation boundary, while operational feasibility and human authorization govern how predictions inform dispatch decisions.