An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study.
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 app...
| Publicado en: | Journal of Computer Applications in Archaeology Vol. 4; no. 1; pp. 110 - 125 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Ubiquity Press
2021
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=155673602&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 155673602 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 25148362 MPQT jtl: Journal of Computer Applications in Archaeology issn: 25148362 maglogo: N pubinfo: dt: 2021 vid: 4 iid: 1 pid: 83901 pub: Ubiquity Press artinfo: ui: 155673602 10.5334/jcaa.71 ppf: 110 ppct: 15 formats: tig: atl: An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study. aug: au: CASTIELLO, MARIA ELENA TONINI, MARJ affil: Institute of Archaeological Sciences, University of Bern, CH-3012 Bern, Switzerland Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, CH-1015 Lausanne, Switzerland su: Random forest algorithms Machine learning Cultural property Archaeological excavations Political organizations sug: subj: Random forest algorithms Machine learning Cultural property Archaeological excavations Political organizations keyword: Canton of Zurich Cultural Heritage Management Locational Patterns Machine Learning Roman Settlements ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
|---|