@article{1443935,
      note = {"Doctoral Thesis accepted by University of Bern,  Switzerland."},
      author = {Castiello, Maria Elena.},
      url = {http://library.usi.edu/record/1443935},
      title = {Computational and machine learning tools for archeological  site modeling /},
      publisher = {Springer,},
      abstract = {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.},
      doi = {https://doi.org/10.1007/978-3-030-88567-0},
      recid = {1443935},
      pages = {1 online resource},
      address = {Cham, Switzerland :},
      year = {2022},
}