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Table 9

Statistical indices using the applied soft computing models for modeling the hydraulic jump length (L_j) in the training and validation phases

Stage		ANFIS			RANFIS
		Statistical parameters			Statistical parameters
	Method	RMSE (m)	R²	IA	RMSE (m)	R²	IA
Train	ANFIS-PSO	0.0495	0.863	0.962	0.0497	0.861	0.962
	ANFIS-FA	0.0484	0.869	0.964	0.0506	0.857	0.961
	ANFIS-GA	0.0468	0.877	0.966	0.0501	0.859	0.961
	ANFIS	0.054	0.837	0.954	0.0562	0.823	0.949
	ANFIS-MFO	0.0577	0.813	0.946	0.064	0.771	0.931
	ANFIS-WOA	0.0705	0.721	0.914	0.0711	0.717	0.912
Validation	ANFIS-PSO	0.0432	0.887	0.97	0.0466	0.869	0.965
	ANFIS-FA	0.0498	0.849	0.959	0.0461	0.87	0.964
	ANFIS-GA	0.049	0.854	0.96	0.0531	0.829	0.95
	ANFIS	0.0527	0.831	0.953	0.0557	0.812	0.947
	ANFIS-MFO	0.0603	0.782	0.933	0.0661	0.737	0.916
	ANFIS-WOA	0.0748	0.661	0.89	0.0751	0.658	0.889

Stage		ANFIS			RANFIS
		Statistical parameters			Statistical parameters
	Method	RMSE (m)	R²	IA	RMSE (m)	R²	IA
Train	ANFIS-PSO	0.0495	0.863	0.962	0.0497	0.861	0.962
	ANFIS-FA	0.0484	0.869	0.964	0.0506	0.857	0.961
	ANFIS-GA	0.0468	0.877	0.966	0.0501	0.859	0.961
	ANFIS	0.054	0.837	0.954	0.0562	0.823	0.949
	ANFIS-MFO	0.0577	0.813	0.946	0.064	0.771	0.931
	ANFIS-WOA	0.0705	0.721	0.914	0.0711	0.717	0.912
Validation	ANFIS-PSO	0.0432	0.887	0.97	0.0466	0.869	0.965
	ANFIS-FA	0.0498	0.849	0.959	0.0461	0.87	0.964
	ANFIS-GA	0.049	0.854	0.96	0.0531	0.829	0.95
	ANFIS	0.0527	0.831	0.953	0.0557	0.812	0.947
	ANFIS-MFO	0.0603	0.782	0.933	0.0661	0.737	0.916
	ANFIS-WOA	0.0748	0.661	0.89	0.0751	0.658	0.889