Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Table 1

Results of different modeling for the Hambantota Port at daily scales.

Input	Output	Case study	Stage	Model	Efficiency criteria				P-value
					NSE	MAE	RMSE	R
Ut	Hst	Hambantota Port	Train	ANN	0.74	0.24	0.06	0.37	0.017
			Test		0.66	0.27	0.12	0.34	0.021
		Sanmen Bay	Train		0.70	0.26	0.08	0.34	0.024
			Test		0.63	0.31	0.14	0.30	0.037
		Hambantota Port	Train	WANN	0.93	0.18	0.01	0.43	<0.001
			Test		0.90	0.21	0.04	0.40	<0.001
		Sanmen Bay	Train		0.91	0.20	0.03	0.42	<0.001
			Test		0.87	0.22	0.06	0.38	0.001
		Hambantota Port	Train	M5	0.72	0.23	0.11	0.39	0.011
			Test		0.64	0.28	0.13	0.36	0.033
		Sanmen Bay	Train		0.69	0.26	0.07	0.35	0.021
			Test		0.60	0.30	0.16	0.30	0.051
		Hambantota Port	Train	Wavelet-M5	0.94	0.17	0.03	0.46	<0.001
			Test		0.89	0.20	0.06	0.43	0.002
		Sanmen Bay	Train		0.88	0.18	0.04	0.42	<0.001
			Test		0.84	0.22	0.08	0.38	0.002