Computational and Machine Learning Tools for Archaeological Site Modeling
Sierra Castillo, María Elena
Computational and Machine Learning Tools for Archaeological Site Modeling by Maria Elena Castiello - First edition 2022 - 1 recurso en línea (XVIII, 296 páginas) 159 ilustraciones, 139 ilustraciones a color - Springer Theses Recognizing Outstanding Ph.D. Research 2190-5061 .
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.
9783030885670
10.1007/978-3-030-88567-0 doi
Aprendizaje automático
Arqueología--Proceso de datos
Diseño asistido por ordenador
CC80.4 / 2022 EB
Computational and Machine Learning Tools for Archaeological Site Modeling by Maria Elena Castiello - First edition 2022 - 1 recurso en línea (XVIII, 296 páginas) 159 ilustraciones, 139 ilustraciones a color - Springer Theses Recognizing Outstanding Ph.D. Research 2190-5061 .
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.
9783030885670
10.1007/978-3-030-88567-0 doi
Aprendizaje automático
Arqueología--Proceso de datos
Diseño asistido por ordenador
CC80.4 / 2022 EB