Application of artificial neural network in flow and transport simulations
Artificial neural network (ANN) is considered to be a powerful tool for solving groundwater problems which require a large number of flow and contaminant transport (GFCT) simulations. Often, GFCT models are nonlinear, and they are difficult to solve using traditional numerical methods to simulate specific input–output responses. In order to avoid these difficulties, ANN may be used to simulate the GFCT responses explicitly. In this manuscript, recent research related to the application of ANN in simulating GFCT responses is critically reviewed, and six research areas are identified. In order to study these areas, a one-dimensional unsaturated flow and transport scenario was developed, and ANN was used to simulate the effects of specific GFCT parameters on overall results. Using these results, ANN concepts related to architecture, sampling, training, and multiple function approximations are studied, and ANN training using back-propagation algorithm (BPA) and genetic algorithm (GA) are compared. These results are summarized, and appropriate conclusions are made.