Date of Award

Fall 11-21-2025

Document Type

Masters Thesis

Degree Name

M.S. in Computer Science

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science

First Advisor

Stephen Hutt

Second Advisor

Kerstin Haring

Third Advisor

Kara Nance

Fourth Advisor

Guiming Zhang

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Keywords

Archaeological predictive modeling, Artificial intelligence (AI), Explainable AI, Geographic information science (GIS), Machine learning, Mesoamerica

Abstract

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to develop, a roadblock that can prevent non-technical professionals from fully understanding numerical results. We created a model that solves all three of these problems, a succinct meta-model that utilizes features derived from both geographical data points and quantitative representations of abstract theoretical criteria, which can compare the importance of various domains of features, and incorporate eXaplainable AI features for more transparent decision making. In the end, its result can be further extrapolated to predict the locations of undiscovered archaeological sites in the historically underrepresented region of Mexico.

JEL— C45, C51, C52, Q01, R14, R15

Copyright Date

11-2025

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Peter Stamm

Provenance

Received from ProQuest

File Format

application/pdf

Language

English (eng)

Extent

56 pgs

File Size

8.9 MB

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