Table 3.

Overall model performance metrics (R², MSE, RMSE, MAE) and composite ranking scores for all sixteen models evaluated. Models are ordered by composite score (descending).

Model	R2	MSE	RMSE	MAE	Score	Rank
Statistical distribution models
GEVMLE	0.9677	2.285	1.512	0.907	1.000	1
GEVLM	0.9456	3.849	1.962	0.908	0.970	2
GLLM	0.9346	4.626	2.151	1.030	0.946	3
GPMLE	0.9322	4.798	2.190	1.213	0.928	4
GPPM	0.9281	5.088	2.256	1.281	0.918	5
GPM	0.9305	4.917	2.217	1.443	0.907	6
GLM	0.9144	6.051	2.460	1.253	0.904	7
EMLE	0.9142	6.071	2.464	1.374	0.894	8
LLLM	0.9044	6.763	2.601	1.263	0.892	9
SEMLE	0.9105	6.330	2.516	1.390	0.889	10
ELM	0.9113	6.276	2.505	1.447	0.885	11
EPM	0.9103	6.341	2.518	1.469	0.882	12
LLPM	0.8731	8.978	2.996	1.372	0.850	13
Machine learning models
RF	0.2799	50.925	7.136	3.007	0.218	14
XGBR	0.2577	52.494	7.245	3.506	0.159	15
SVM	0.0867	64.587	8.037	3.888	0.000	16