Experimental evaluation of artificial neural network for predicting drainage water and groundwater salinity at various drain depths and spacing

Drainage design parameters of drain depth and spacing are the pivotal factors affect the drain water quality by radial flow of underground water. In this study, artificial neural network modeling has been employed with Levenberg–Marquardt learning algorithm followed by Sigmoid Axon transfer function to anticipate the temporal changes in shallow groundwater and drain water salinities at various depths and spaces of drain installation. Calibration and validation of the model results were carried out based on data obtained from experimental model with 1.8 m long, 1 m wide, and 1.2 m high. In the model, drains were installed at 20, 40, and 60 cm depths and 60, 90, and 180 cm spaces. The values of error indices of RMSE and SE as well as R² between measured and simulated shallow groundwater salinities were 5.27 dS/m, 0.12, and 0.96, respectively. These indexes for drain water salinity were obtained 0.72 dS/m, 0.096, and 0.99, respectively. The key results revealed that artificial neural network methodology has a reasonable accuracy on simulating temporal shallow groundwater and drain water salinities in different drain depth and drain spacing.