Estimating anisotropic aquifer parameters by artificial neural networks

In recent years, many approaches have been developed using the artificial neural networks (ANN) model incorporated with the Theis analytical solution to estimate the effective hydrological parameters for homogeneous and isotropic porous media, such as the Lin and Chen approach (ANN approach) and the principal component analysis (PCA)-ANN approach. The above methods assume a full superimposition of the type curve and the observed drawdown and try to use the first time-drawdown data as a match point to make a fine approximation of the effective parameters. However, using first time-drawdown data or early time-drawdown data does not always allow for an accurate estimation of the hydrological parameters, especially for heterogeneous and anisotropic aquifers. Therefore, this article corrects the concept of the superimposed plot by modifying the ANN approach and the PCA-ANN approach, as well as incorporating the Papadopoulos analytical solution, to estimate the transmissivities and storage coefficient for anisotropic, homogeneous aquifers. The ANN model is trained with 4000 training sets of the well function, and tested with 1000 sets and 300 sets of synthetic time-drawdown generated from the homogeneous and heterogeneous parameters, respectively. In situ observation data from the time-drawdown at station Shi-Chou on the Choushui River alluvial fan, Taiwan, is further adopted to test the applicability and reliability of the proposed methods, as well as provide a basis for comparison with the Straight-line method and the Type-curve method. Results suggest that both of the modified methods perform better than the original ones, and using late time-drawdown to optimize the effective parameters is shown to be better than using early time-drawdown. Additionally, results indicate that the modified ANN approach is better than the modified PCA-ANN approach in terms of precision, while the efficiency of the modified PCA-ANN approach is approximately three times better than that of the modified ANN approach. Copyright (C) 2010 John Wiley & Sons, Ltd.