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Artificial Neural Networks in Subsurface Characterization

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Artificial Neural Networks in Hydrology

Part of the book series: Water Science and Technology Library ((WSTL,volume 36))

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Abstract

Successful management of subsurface environmental resources is highly dependent on the quality of relevant characterization and monitoring. In particular, the ability to identify patterns in raw data measurements and to extract valuable spatial information from different measurement sources is critical to answering questions posed by management. These questions range from “what are the pathways by which receptors may be exposed to (human or ecological) health risks” to “can I determine from existing measurement data whether a buried facility is failing”.

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Author information

Authors and Affiliations

  1. Subterranean Research, Inc., Burlington, Vermont, 05401, USA

    Donna M. Rizzo & David E. Dougherty

Authors
  1. Donna M. Rizzo
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  2. David E. Dougherty
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Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, Indiana, USA

    R. S. Govindaraju  & A. Ramachandra Rao  & 

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Rizzo, D.M., Dougherty, D.E. (2000). Artificial Neural Networks in Subsurface Characterization. In: Govindaraju, R.S., Rao, A.R. (eds) Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9341-0_7

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  • DOI: https://doi.org/10.1007/978-94-015-9341-0_7

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5421-0

  • Online ISBN: 978-94-015-9341-0

  • eBook Packages: Springer Book Archive

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