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Computational and Machine Learning Tools for Archaeological Site Modeling

  • Book
  • © 2022

Overview

Authors:
  1. Maria Elena Castiello

    1. Institute of Archaeological Sciences, University of Bern, Bern, Switzerland

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  • Nominated as an outstanding PhD thesis by the University of Bern, Switzerland
  • Describes novel methods for investigating archaeological settlement patterns and locational preference choices
  • Proposes a machine learning model for archaeological site prediction and detection

Part of the book series: Springer Theses (Springer Theses)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Section I

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Maria Elena Castiello
      Pages 3-19

  3. Section II

    1. Front Matter

      Pages 21-21
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    2. Space, Environment and Quantitative Approaches in Archaeology

      • Maria Elena Castiello
      Pages 23-32

    3. Predictive Modeling

      • Maria Elena Castiello
      Pages 33-63

  4. Section III

    1. Front Matter

      Pages 65-65
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    2. Materials and Data

      • Maria Elena Castiello
      Pages 67-109

    3. Modeling Approach

      • Maria Elena Castiello
      Pages 111-148

  5. Section IV

    1. Front Matter

      Pages 149-149
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    2. Results and Discussion

      • Maria Elena Castiello
      Pages 151-171

    3. Conclusions

      • Maria Elena Castiello
      Pages 173-182

  6. Back Matter

    Pages 183-296
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Keywords

About this book

This book describes a novel machine-learning based approach   to answer some traditional archaeological problems, relating to archaeological site detection and site locational preferences. Institutional data collected from six Swiss regions (Zurich, Aargau, Grisons, Vaud, Geneva and Fribourg) have been analyzed with an original conceptual framework based on the Random Forest algorithm. It is shown how the algorithm can assist in the modelling process in connection with heterogeneous, incomplete archaeological datasets and related cultural heritage information. Moreover, an in-depth review of past and more recent works of quantitative methods for archaeological predictive modelling is provided. The book guides the readers to set up their own protocol for: i) dealing with uncertain data, ii) predicting archaeological site location, iii) establishing environmental features importance, iv) and suggest a model validation procedure. It addresses both academics and professionals in archaeology and cultural heritage management, and offers a source of inspiration for future research directions in the field of digital humanities and computational archaeology.

 




























Authors and Affiliations

  • Institute of Archaeological Sciences, University of Bern, Bern, Switzerland

    Maria Elena Castiello

Bibliographic Information

  • Book Title: Computational and Machine Learning Tools for Archaeological Site Modeling

  • Authors: Maria Elena Castiello

  • Series Title: Springer Theses

  • DOI: https://doi.org/10.1007/978-3-030-88567-0

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-88566-3Published: 25 January 2022

  • Softcover ISBN: 978-3-030-88569-4Published: 26 January 2023

  • eBook ISBN: 978-3-030-88567-0Published: 24 January 2022

  • Series ISSN: 2190-5053

  • Series E-ISSN: 2190-5061

  • Edition Number: 1

  • Number of Pages: XVIII, 296

  • Number of Illustrations: 20 b/w illustrations, 139 illustrations in colour

  • Topics: Computational Intelligence, Archaeology, Machine Learning

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