Computational Geosciences

, Volume 22, Issue 4, pp 1135–1148 | Cite as

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

  • Tahereh Azari
  • Nozar SamaniEmail author
Original Paper


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.


Aquifer parameters Well function Multi-layer perceptron Principal component analysis (PCA) Pumping test 


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Constructive comments and suggestions provided by the associate Editor Prof. Ibrahim Hoteit and two anonymous reviewers are greatly appreciated.

Funding Information

This research was supported by the Office of Research Vice Chancellor of Shiraz University, Iran, grant number 11/480.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Earth Sciences, College of SciencesShiraz UniversityShirazIran

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