An explorative application of random forest algorithm for archaeological predictive modeling. A swiss case study
Open accessAbstract
The present work proposes an innovative approach to surveys and demonstrates the effectiveness of bringing together traditional archaeological questions, such as the exploration and the analysis of settlement patterns, with the most innovative technologies related to Machine Learning. Namely, we applied Random Forest, an ensemble learning method based on decision trees, to perform archaeological predictive modeling (APM) for the Canton of Zurich, in Switzerland. This was done based on a dataset of known archaeological sites dating back to the Roman Age. The APM represents an automated decision-making and probabilistic reasoning tool that is relevant for archaeological risk assessment and cultural heritage management. Machine learning-based approaches can learn from data and make predictions, starting from the acquired knowledge, through the modeling of the hidden relationships between a set of observations, representing the dependent variable (i.e. the archeological sites), and the independent variables (i.e. the geo-environmental features prone to influence the site locations). The main objective of the present study is to assess the spatial probability of presence for Roman settlements within the study area. As results, we produced: 1) a probability map, expressing the likelihood of finding a Roman site at different locations; 2) the importance ranking of the geo-environmental features influencing the presence of the archeological sites. These outputs in our results are of paramount importance, not only in verifying the reliability of the data, but also in stimulating experts in different ways. Also, these results help evaluate the benefits and constraints of using such innovative techniques and, ultimately, help explore the performance of machine learning-based models in processing archaeological information.
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Cited by
Sorted by citations
Wang, J., Soroush, M., Maziar, S., & Khazraee, E. (2026) · Journal of Archaeological Method and Theory · doi:10.1007/s10816-025-09733-5 · Cited by 3 · see citing works
- Authors
- Castiello, M. E., & Tonini, M.
- Venue
- Journal of Computer Applications in Archaeology
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2021
- Volume
- 4
- Issue
- 1
- Pages
- 110-125
- ISSN
- 2514-8362 (ISSN-L)
- OpenAlex
- W3165170369
- Cited by
- 29
- References
- 48
- Citations by year
- 2026: 9 · 2025: 7 · 2024: 7 · 2023: 5 · 2022: 1
- FWCI
- 7.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Computer Applications in Archaeology (publishedVersion, cc-by)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IRIS (submittedVersion, cc-by)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Space and Planetary Science › Archaeological Research and Protection
- Physical Sciences › Engineering › Ocean Engineering › Geophysical Methods and Applications
- Social Sciences › Cultural Studies › Archaeology and Cultural Heritage
- Keywords
- archaeological predictive modeling, random forest, Roman settlement, settlement patterns, probability maps, feature importance ranking, archaeological risk assessment, heritage management, Canton of Zurich, Roman Age
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