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”.
Keywords
- Ordinary Kriging
- Interpolation Point
- Lawrence Livermore National Laboratory
- Linear Discriminant Function
- Soft Data
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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
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