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Computational and machine learning tools for archaeological site modeling

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational and machine learning tools for archaeological site modelingby Maria Elena Castiello.
Author:	Castiello, Maria Elena.
Published:	Cham :Springer International Publishing :2022.
Description:	xviii, 296 p. :ill., digital ;24 cm.
Notes:	"Doctoral Thesis accepted by University of Bern, Switzerland."
Contained By:	Springer Nature eBook
Subject:	ArchaeologyData processing.
Online resource:	https://doi.org/10.1007/978-3-030-88567-0
ISBN:	9783030885670$q(electronic bk.)

Computational and machine learning tools for archaeological site modeling
Castiello, Maria Elena.

Computational and machine learning tools for archaeological site modeling[electronic resource] /by Maria Elena Castiello. - Cham :Springer International Publishing :2022. - xviii, 296 p. :ill., digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral Thesis accepted by University of Bern, Switzerland."

Introduction -- Space, Environment and Quantitative approaches in Archaeology -- Predictive Modeling -- Materials and Data.

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.

ISBN: 9783030885670$q(electronic bk.)

Standard No.: 10.1007/978-3-030-88567-0doiSubjects--Topical Terms:

582222
Archaeology
--Data processing.

LC Class. No.: CC80.4 / .C37 2022

Dewey Class. No.: 930.10285631

Computational and machine learning tools for archaeological site modeling
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