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From the existing 80 patterns of inclined slope controlled structure data, 53 patterns were used for training and 27 patterns were considered for testing. Table 5 sums up the corresponding error statistics for the GEP and FFNN models. From the table, it is observed that the models corresponding to the MI3 configuration present the highest accuracies. Figure 3(c) illustrates the observed vs. simulated scour depth values of GEP and FFNN models.

Table 5

Testing statistics of the GEP and FFNN models for inclined slope controlled structure

Input configuration	Model	CC	R²	RMSE	MAE	Hidden neuron no.
MI1	GEP	0.211	0.399	0.311	0.284	–
MI1	FFNN	0.198	0.295	0.335	0.282	2
MI2	GEP	0.502	0.514	0.282	0.246	–
MI2	FFNN	0.613	0.666	0.239	0.202	2
MI3	GEP	0.803	0.769	0.198	0.164	–
MI3	FFNN	0.833	0.835	0.175	0.131	3
MI4	GEP	0.633	0.699	0.221	0.186	–
MI4	FFNN	0.702	0.745	0.208	0.185	5
MI5	GEP	0.588	0.679	0.234	0.192	–
MI5	FFNN	0.598	0.741	0.194	0.167	5
MI6	GEP	0.657	0.717	0.223	0.186	–
MI6	FFNN	0.635	0.677	0.232	0.197	7
MI7	GEP	0.786	0.819	0.279	0.239	–
MI7	FFNN	0.509	0.551	0.268	0.202	3
MI8	GEP	0.691	0.733	0.212	0.174	–
MI8	FFNN	0.609	0.658	0.237	0.197	3
MI9	GEP	0.687	0.724	0.209	0.169	–
MI9	FFNN	0.702	0.751	0.225	0.175	5

Input configuration	Model	CC	R²	RMSE	MAE	Hidden neuron no.
MI1	GEP	0.211	0.399	0.311	0.284	–
MI1	FFNN	0.198	0.295	0.335	0.282	2
MI2	GEP	0.502	0.514	0.282	0.246	–
MI2	FFNN	0.613	0.666	0.239	0.202	2
MI3	GEP	0.803	0.769	0.198	0.164	–
MI3	FFNN	0.833	0.835	0.175	0.131	3
MI4	GEP	0.633	0.699	0.221	0.186	–
MI4	FFNN	0.702	0.745	0.208	0.185	5
MI5	GEP	0.588	0.679	0.234	0.192	–
MI5	FFNN	0.598	0.741	0.194	0.167	5
MI6	GEP	0.657	0.717	0.223	0.186	–
MI6	FFNN	0.635	0.677	0.232	0.197	7
MI7	GEP	0.786	0.819	0.279	0.239	–
MI7	FFNN	0.509	0.551	0.268	0.202	3
MI8	GEP	0.691	0.733	0.212	0.174	–
MI8	FFNN	0.609	0.658	0.237	0.197	3
MI9	GEP	0.687	0.724	0.209	0.169	–
MI9	FFNN	0.702	0.751	0.225	0.175	5

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