Accession Number : AD1040975

Title :   Cultural Resource Predictive Modeling

Descriptive Note : Technical Report


Full Text :

Report Date : 01 Oct 2017

Pagination or Media Count : 103

Abstract : This document surveyed the range members to determine what level was being used. The ultimate goal is to have a compliant cultural resource program and provide support to the test ranges for their missions. This document will provide information such as lessons learned, points of contact, and resources to the range cultural resource managers. Objective/Scope: Identify existing cultural resource predictive models and lessons learned from predictive modeling. Provide a list of points of contact. Deliverable: Report outlining what cultural resource predictive modeling is, including benefits and limitations; list of models that currently exist, including benefits and limitations; lessons learned from previous predictive modeling efforts; and subject matter experts at member ranges. Benefit: Virtually every member range has cultural resources on the range. Predicting the location of CRs would benefit the range user when planning future test programs. Cultural resource predictive modeling is rather complex. When faced with the challenges, this document would accelerate the timeline for member ranges. Archaeological predictive models use prior knowledge to predict the expected nature and distribution of the archaeological record. There is no one kind of predictive model, although the military has many models designed to predict the location of sites discovered through conventional survey techniques. Note: models are built for areas needed, topographical considerations, and type of CR. In addition to predicting site location, models can be constructed to predict archaeological data quality, significance, the potential of encountering buried sites, and other features important to the management of CRs on military lands. The CRPM process relies on using prior knowledge already gained about the archaeological record. Verification of models through ground survey needs to be part of the process to ensure accuracy of the predictive modeling.

Descriptors :   predictive modeling , environment , geographic information systems , surveys , water resources , resource management , cultural resources , historic sites , lessons learned , training , test facilities , COMPUTATIONAL ARCHAEOLOGY , Ranges (Facilities)

Subject Categories : Humanities and History
      Test Facilities, Equipment and Methods
      Logistics, Military Facilities and Supplies

Distribution Statement : APPROVED FOR PUBLIC RELEASE