Predicting the Impact of Subsurface heterogeneous Hydraulic Conductivity on the Stochastic Behavior of Well Draw down in a Confined Aquifer Using Artificial Neural Networks

Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these techniques require lots of computational and numerical efforts according to the various researchers' comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to predict the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.