Evidence item

To address this limitation, we propose a Hybrid Linear+XGBoost framework that combines physically interpretable geometric corrections with nonlinear residual learning. Under leave-one-scenario-out cross-validation, the hybrid model achieves RMSE = 2.97 dB, a 24.0% improvement over the CI baseline. Crucially, while pure ensemble methods (Random Forest, Gradient Boosting, XGBoost) exhibit performance degradation up to 1.05 dB when extrapolating to unseen geometric configurations, the hybrid architecture demonstrates a 0.35 dB improvement, achieving better accuracy on novel corridor configurations than on interpolated data. Corridor width emerged as the dominant factor influencing shadow fading.