Neural-network approach to the determination of aquifer parameters

Abd Rashid, Kaufui Wong, Kau Fui Vincent Wong

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76 Scopus citations

Abstract

A new approach to determine aquifer parameter values from aquifer-test data has been developed that uses the pattern-matching capability of a neural network. The network is trained to recognize patterns of normalized drawdown data as input and the corresponding aquifer parameters as output. The Theis and Hantush-Jacob solutions for confined and leaky-confined aquifer conditions are used to derive the input patterns based on the parameter values selected from predetermined ranges. The trained network produces output of aquifer parameter values when it receives the aquifer-test data as the input patterns. The results obtained from this new approach are in good agreement with published results using other techniques. The advantages of the present approach are the automated process of obtaining aquifer parameter values and the ability of the network to associate drawdown to the corresponding Theis and Hantush-Jacob solutions.

Original languageEnglish
Pages (from-to)164-166
Number of pages3
JournalGround Water
Volume30
Issue number2
StatePublished - Mar 1 1992

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ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Water Science and Technology

Cite this

Rashid, A., Wong, K., & Vincent Wong, K. F. (1992). Neural-network approach to the determination of aquifer parameters. Ground Water, 30(2), 164-166.