Table 2. Neural network architectures...

Table 2.

Neural network architectures and hyperparameters used

Parameter	NN-1D	NN-2D	NN-3D
Model type	Sequential	Sequential	Sequential
Input features	2	2	3
Layer 1 units	32	32	32
Layer 1 activation	tanh	tanh	tanh
Layer 2 units	8	8	16
Layer 2 activation	tanh	tanh	tanh
Layer 3 units	4	4	8
Layer 3 activation	tanh	tanh	tanh
Layer 4 units	–	–	4
Layer 4 activation	–	–	tanh
Output layer units	1	1	1
Output layer activation	ReLU	ReLU	ReLU
Loss Function	MSE	MSE	MSE
Optimizer	Adam	Adam	Adam
Metric	MAPE, RMSE	MAPE, RMSE	MAPE, RMSE
Epochs	2,000	2,000	2,000
Batch size	1,064 (256 in k-fold)	1,064 (256 in k-fold)	1,064 (256 in k-fold)
Shuffling	Enabled	Enabled	Enabled
Validation split	0.4	0.4	0.4
k-fold c.v., RMSE training-validation	0.056–0.056	0.12–0.14	1.29–1.31
Accuracy	97%	96%	85%

Parameter	NN-1D	NN-2D	NN-3D
Model type	Sequential	Sequential	Sequential
Input features	2	2	3
Layer 1 units	32	32	32
Layer 1 activation	tanh	tanh	tanh
Layer 2 units	8	8	16
Layer 2 activation	tanh	tanh	tanh
Layer 3 units	4	4	8
Layer 3 activation	tanh	tanh	tanh
Layer 4 units	–	–	4
Layer 4 activation	–	–	tanh
Output layer units	1	1	1
Output layer activation	ReLU	ReLU	ReLU
Loss Function	MSE	MSE	MSE
Optimizer	Adam	Adam	Adam
Metric	MAPE, RMSE	MAPE, RMSE	MAPE, RMSE
Epochs	2,000	2,000	2,000
Batch size	1,064(256 in k-fold)	1,064(256 in k-fold)	1,064(256 in k-fold)
Shuffling	Enabled	Enabled	Enabled
Validation split	0.4	0.4	0.4
k-fold c.v., RMSE training-validation	0.056–0.056	0.12–0.14	1.29–1.31
Accuracy	97%	96%	85%

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