Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument
Open accessAbstract
Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lack of theoretical insight to explain the responses of past land use to predictor variables. Here we address these critiques and evaluate the predictive power of four statistical approaches widely used in ecological modeling-generalized linear models, generalized additive models, maximum entropy, and random forests-to predict the locations of Formative Period (2100-650 BP) archaeological sites in the Grand Staircase-Escalante National Monument. We assess each modeling approach using a threshold-independent measure, the area under the curve (AUC), and threshold-dependent measures, like the true skill statistic. We find that the majority of the modeling approaches struggle with archaeological datasets due to the frequent lack of true-absence locations, which violates model assumptions of generalized linear models, generalized additive models, and random forests, as well as measures of their predictive power (AUC). Maximum entropy is the only method tested here which is capable of utilizing pseudo-absence points (inferred absence data based on known presence data) and controlling for a non-representative sampling of the landscape, thus making maximum entropy the best modeling approach for common archaeological data when the goal is prediction. Regression-based approaches may be more applicable when prediction is not the goal, given their grounding in well-established statistical theory. Random forests, while the most powerful, is not applicable to archaeological data except in the rare case where true-absence data exist. Our results have significant implications for the application of predictive models by archaeologists for research and conservation purposes and highlight the importance of understanding model assumptions.
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- Authors
- Yaworsky, P. M., Vernon, K. B., Spangler, J. D., Brewer, S. C., & Codding, B. F.
- ORCID
- Yaworsky, Peter M.
- Venue
- PLOS One
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-10-01
- Volume
- 15
- Issue
- 10
- Pages
- e0239424
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W3090779497
- Cited by
- 104
- References
- 87
- Citations by year
- 2026: 22 · 2025: 26 · 2024: 19 · 2023: 16 · 2022: 15 · 2021: 4 · 2019: 1 · 2018: 1
- FWCI
- 12.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Utah
- U.S. Bureau of Land Management
- Topics
- Physical Sciences › Earth and Planetary Sciences › Space and Planetary Science › Archaeological Research and Protection
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Social Sciences › Cultural Studies › Archaeology and Cultural Heritage
- Keywords
- archaeological site prediction, predictive modeling, Grand Staircase-Escalante National Monument, Formative period, maximum entropy, random forest, generalized additive models, generalized linear models, model evaluation, area under the curve, true skill statistic, cultural resource management, land use
- MeSH terms
- Machine Learning, Archaeology, Regression Analysis, Models, Statistical, Area Under Curve
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