Modeling the Neuman’s well function by an artificial neural network for the determination of unconfined aquifer parameters

Abstract

An artificial neural network is designed as an improved alternative approach to the conventional type-curve matching technique for the determination of unconfined aquifer parameters. The network is implemented in a six-step protocol consisted of input selection, data splitting, design of network architecture, determination of network structure, network training, and network validation. The network is trained for the well function of unconfined aquifers by the back-propagation technique, adopting the Levenberg-Marquardt optimization algorithm. By applying a principal component analysis (PCA) on the training input data and through a trial-and-error procedure, the structure of the network is optimized with the topology of (3 × 6 × 3). The replicative, predictive, and structural validity of the developed network are evaluated with synthetic and real field data. The network eliminates graphical error inherent in the type-curve matching technique and provides an automatic and fast procedure for aquifer parameter estimation, particularly when analyzing many alternative pumping tests routinely obtained from continuous data loggers/data collection systems.