Algorithmic Classification and Statistical Modelling of Coastal Settlement Patterns in Mesolithic South-Eastern Norway

Roalkvam, Isak

doi:10.5334/jcaa.60

Cited by 10 publications

(8 citation statements)

References 49 publications

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“…With the advent of Machine Learning techniques, such separation is routinely possible, using iterative methodologies that improve on their results through validation of reliable training data. The utility of such approaches has been seen more widely in Archaeology, including towards remote sensing and prediction or classification of archaeological sites 11 – 13 , the recording and creation of artefact typologies 14 – 19 , and more recently for lithic sourcing 20 – 24 . For this latter topic, these techniques promise more powerful approaches to the separation of geological samples and increased accuracy over classical statistical techniques.…”

Section: Introductionmentioning

confidence: 99%

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Elliot

Morse

Smythe³

et al. 2021

Sci Rep

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It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.

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Section: Introductionmentioning

confidence: 99%

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Elliot

Morse

Smythe³

et al. 2021

Sci Rep

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“…The research results indicate that LR is the most effective way, though archaeological researchers have started to explore more sophisticated models. Based on research conducted by Märker [47], Peter M. Yaworsky [48], Roalkvam [48] and others, some have elaborated machine learning in archaeological site prediction, and the method is more accurate than other models. It is feasible for machine learning to be a model for archaeological site prediction.…”

Section: Comparative Analysis Of Rf and Other Predictive Methodsmentioning

confidence: 99%

A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

Chen

et al. 2022

Sustainability

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Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It is conducive to archaeological excavation and reveals the progress of human social civilization. Xiangyang City is the focus of this paper. We selected eight geographical variables as the influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile curvature, and distance from water. With them, we randomly obtained 260 non-site points at the ratio of 1:1 between site points and non-site points based on the 260 excavated archaeological sites and constructed a sample set of geospatial data and the archaeological based on logistic regression (LR). Using 10-fold cross-validation, we trained and tested the model to select the best samples. Thus, the quantitative relationship between the archaeological sites and geographical variables was established. As a result, the Area Under the Curve (AUC) of the LR model is 0.797 and its accuracy is 0.897 in the study. A geographical detector unveils that the three influencing variables of Distance from water, elevation and Plan Curvature top the chart. The archaeological under LR is highly stable and accurate. The geographical variables constitute crucial variables in the archaeological.

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“…På et overordnet nivå er det et stort potensial i å utnytte registreringsdata i enda større grad for å undersøke f.eks. lokalisering og landskapsbruk (Roalkvam 2020). Til tross for utfordringer knyttet til datering av lokaliteter er det et rikt datamateriale her som kan settes i sammenheng med de utgravde lokalitetene.…”

Section: Bosetningsmønster Og Landskapsbrukunclassified

Steinalderen i Sørøst-Norge: Faglig program for steinalderundersøkelser ved Kulturhistorisk museum

Damlien¹,

Berg-Hansen²,

Melheim³

et al. 2021

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Steinalderen i Sørøst-Norge. Faglig program for steinalderundersøkelser ved Kulturhistorisk museum inneholder en helhetlig oversikt over utgravinger av 430 boplasser, begravelser, depoter, fangstanlegg m.m. fra år 2000 til 2017, og presenterer en bred og oppdatert redegjørelse for forskningsstatus knyttet til steinalderen (ca. 9500–1700 f.Kr.) i regionen. Den manglede kunnskapen har dannet utgangspunkt for formuleringen av fire framtidige satsingsområder: Teknologi Bosetning og landskapsbruk Ressursgrunnlag og økonomi Ritualer og ritualisert praksis I dette faglige programmet for steinalderundersøkelser presenteres konkrete forslag til strategier og tiltak som vil bidra til å tette disse kunnskapshullene, skape faglig utvikling og danne grunnlag for videre forskning. Steinalderen i Sørøst-Norge er skrevet for forskere, forvaltere, studenter og andre som vil gjøre dypdykk i vår eldste forhistorie. Boken vil være et viktig verktøy for planlegging og gjennomføring av kommende steinalderutgravninger ved Kulturhistorisk museum. Den vil også danne et grunnlag for de rådene museet gir i forbindelse med forvaltningen av våre eldste kulturminner.

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Algorithmic Classification and Statistical Modelling of Coastal Settlement Patterns in Mesolithic South-Eastern Norway

Cited by 10 publications

References 49 publications

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

Steinalderen i Sørøst-Norge: Faglig program for steinalderundersøkelser ved Kulturhistorisk museum

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