Subsurface drain spacing in the unsteady conditions by HYDRUS-3D and artificial neural networks

The methodological process for defining the drainage retention capacity of surface layers under conditions of unsteady-state groundwater flow was demonstrated. An artificial neural network analyst model was advanced based on the information from the well-tested model HYDRUS-2D/3D. Artificial neural network knowledge is reported as an intermittent to physical-based modeling of subsurface water distribution from trickle emitters. Three options are prospected to create input-output functional relations from information created using a numerical model (HYDRUS-2D). Artificial neural networks are a tool for modeling of non-linear systems in various engineering fields. These networks are effective tools for modeling non-linear systems. Each artificial neural network includes an input layer and an output layer between which there are one or some hidden layers. In each layer, there are one or several processing elements or neurons. The neurons of the input layer are independent variables of the understudy issue and the neurons of the output layer are its dependent variables. An artificial neural system, through exerting weight on inputs and by using an activation function, attempts to achieve a desirable output. In this research, in order to calculate the drain spacing in an unsteady state in a region situated in the northeast of Ahwaz, Iran, with different soil properties and drain spacing, the artificial neural networks have been used. The neurons in the input layer were specific yield, hydraulic conductivity, depth of the impermeable layer, and height of the water table in the middle of the interval between the drains in two-time steps. The neurons in the output layer were drain spacing. The network designed in this research included a hidden layer with four neurons. The distance of drains computed via this method had a good agreement with real values and had a high precision in comparison with other methods. This was done for three types of linear activation functions and hyperbolic and sigmoid tangents. The mean error was 0.1455, 0.092, and 0.0491, respectively.