Advances in Artificial Intelligence

Research Article

Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

Selection of the best model from the quick-propagation algorithm.


Model inputs	No. of inputs	Algorithm	Architecture	No. of hidden layer	Training		Testing		Validation
Model inputs	No. of inputs	Algorithm	Architecture	No. of hidden layer	R	DC	R	DC	R	DC

R_t, E_t, T_t	3		[3-2-1]	One	0.710	0.039	0.582	−0.179	0.586	−0.626
Q_t−1, R_t, R_t−1, E_t, E_t−1, T_t, T_t−1	7		[ 7-7-1]	One
Q_t−1, Q_t−2, R_t, R_t−1, R_t−2, E_t, E_t−1, E_t−2, T_t, T_t−1, T_t−2	11		[11-14-1]	One	0.984	0.967	0.981	0.963	0.965	0.922
Q_t−1, Q_t−2, Q_t−3, R_t, R_t−1, R_t−2, R_t−3, E_t, E_t−1, E_t−2,E_t−3, T_t, T_t−1, T_t−2, T_t−3	15		[15-15-1]	One	0.996	0.993	0.977	0.952	0.974	0.944
Q_t−1, Q_t−2, Q_t−3, Q_t−4, R_t, R_t−1, R_t−2, R_t−3, R_t−4, E_t, E_t−1, E_t−2, E_t−3, E_t−4, T_t, T_t−1, T_t−2, T_t−3, T_t−4	19	Quick propagation	[19-30-1]	One	0.997	0.995	0.931	0.854	0.970	0.936
R_t, E_t, T_t	3	Quick propagation	[3-2-2-1]	Two	0.626	−0.499	0.611	−0.187	0.646	−0.233
Q_t−1, R_t, R_t−1, E_t, E_t−1, T_t, T_t−1	7		[7-4-5-1]	Two	0.988	0.975	0.975	0.941	0.985	0.970
Q_t−1, Q_t−2, R_t, R_t−1, R_t−2, E_t, E_t−1, E_t−2, T_t, T_t−1, T_t−2	11		[ 11-6-5-1]	Two
Q_t−1, Q_t−2, Q_t−3, R_t, R_t−1, R_t−2, R_t−3, E_t, E_t−1, E_t−2,E_t−3, T_t, T_t−1, T_t−2, T_t−3	15		[15-11-14-1]	Two	0.998	0.996	0.979	0.948	0.979	0.950
Q_t−1, Q_t−2, Q_t−3, Q_t−4, R_t, R_t−1, R_t−2, R_t−3, R_t−4, E_t, E_t−1, E_t−2, E_t−3, E_t−4, T_t, T_t−1, T_t−2, T_t−3, T_t−4	19		[19-10-13-1]	Two	0.998	0.960	0.990	0.978	0.978	0.965