Artificial neural networks approach for the determination of aquifer parameters

Abstract

The determination of the aquifer parameters with sufficient accuracy is an important issue in the application of the mathematical models which have been developed for the groundwater systems. Recently, Artificial Neural Networks (ANNs) approach has become popular trend in the solutions of several hydrological problems. ANNs have the ability of learning and processing the introduced data without the need for the full understanding the physical world of the problems at the hand. In this research, ANN approach has been utilized to determine confined aquifer parameters such as transmissivity and storativity. Furthermore, an iterative ANN model has been proposed to determine leaky confined aquifer parameters. The results that have been obtained by ANN models have been also compared to the conventional curve matching procedures that are employed for the determination of aquifer parameters. As a second dimension of this thesis, a numerical experiment has been conducted to contour transmissivity distribution of a hypothetical aquifer by the ANN approach. The performance of the ANN model has been investigated by comparing the solutions of mathematical methods, namely Radial Basis Function and Ordinary Kriging, which are used in data interpolation. As a conclusion, the ANN approach has been successfully applied to determine aquifer parameters. ANN models demonstrate that the ANN approach can be an alternative modeling technique for the solution of various Hydrological problems.