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Expert Systems Archaeological Predictive Model
Transportation Research Record: Journal of the Transportation Research Board (2014)
  • John B. Ripy, University of Kentucky
  • Theodore H. Grossardt, University of Kentucky
  • Michael Shouse, University of Kentucky
  • Philip Mink, University of Kentucky
  • Keiron Bailey, University of Arizona
  • Carl Shields
This paper reports on the deployment of a predictive model that combines spatial analysis and fuzzy logic modeling to translate expert archeological knowledge into predictive surfaces. Analytic predictive archeological models have great utility for state departments of transportation, and some states have invested millions of dollars in such models. However, classic statistical modeling approaches often require too much data and create questions about whether areas are categorized as low probability because (a) there are no sites or (b) no surveys have been conducted there. However, this process can build robust models around typically sparse archeological data and is not subject to spatial bias. These models are intended to lower overall project costs by identifying corridors with a lower probability of having archeological sites, not to supplant field surveys once a corridor has been chosen. Five influencing factors were defined by archeologists and were calculated with the ArcGIS platform. The archeologists then informed a fuzzy logic induction process that was mapped to output probability functions. These data were geocoded into ArcGIS output surfaces that showed the probability of encountering artifacts. The predictive results were tested through a blind control protocol against cleansed archeological data. These models were shown to perform as well as or better than traditional statistical models and required much less data. The Kentucky implementation includes the superior predictive coverage and, more important, a suite of tools to allow the ArcGIS-competent archeologist to design and execute new modeling routines or to build new models. The availability of higher-quality geographic information systems data will also allow archeologists to update the model.
  • Expert Systems Archaeological Predictive Model,
  • fuzzy logic,
  • spatial analysis
Publication Date
Citation Information
John B. Ripy, Theodore H. Grossardt, Michael Shouse, Philip Mink, et al.. "Expert Systems Archaeological Predictive Model" Transportation Research Record: Journal of the Transportation Research Board Vol. 2403 (2014)
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