Author(s): Jitae Kim; Woncheol Cho

Linked Author(s):

Keywords: Keywords: dispersivities; Neural network; Back propagation

Abstract: Aquifer dispersivities in solute transport problems are estimated using the neural network. The multi-layer perceptron neural network model is set and trained for one and two-dimensional problems using the back propagation algorithm. For training, relative concentrations at five points are given as inputs and dispersivities are given as objectives. After training process finished successfully, estimation of dispersivities is carried out with various concentration distributions which have been calculated by solute transport model. Using these concentrations as inputs for the trained neural network model, dispersivities can be obtained. It is shown that the neural network model provided accurate dispersivities.

DOI:

Year: 1999