Evaluation of Surface Drag Interval in the Variable Situations

Abstract

Tools applied in different fields of engineering are artificial neural networks to model non-linear systems which are efficient and useful. Artificial neural networks consist of input and output layers with one or more hidden layers between them. In fact, one or more processing elements or neurons are used in each layer. Input layer neurons are the independent variables studied and output layer neurons are its dependent variables. The artificial nervous system tries to achieve the desired output by applying weight to the inputs and complaining of an activation function. In this study, artificial neural networks were used so as to estimate the unstable drainage distance in an area located in the northeast of Ahvaz with a variety of soil characteristics and drainage distance. Depth of impermeable layer, special yield, height of water table in the middle of the distance between drains in two time stages and hydraulic conductivity are Input layer neurons applied. There was a discharge distance in the output layer of neurons. The network designed consisted of a hidden layer with four neurons. The distance of the drains was in a proper agreement with the real values estimated by this method and was more accurate than other methods