Dynamic models for reconstructing ancient coastal landscapes: the use of the MAXENT algorithm
The availability of detailed environmental data has fueled a rapid increase in predictive modeling of archaeological landscapes and geographic distributions of archaeological evidence allowing the use of a variety of standard statistical techniques. In this paper we introduce the application of statistical and entropical methods in the geo-spatial analysis of the Sinis peninsula in the Gulf of Oristano, as investigated by the university of Sassari.The project was characterized by the use of MAXENT, a general-purpose machine-learning method with a simple and precise mathematical formulation characterized by a number of aspects that make it well-suited for archaeological distribution modeling. Our model was characterized by different markers identified during the archaeological research, such as geophysical and geoarcheological aspects and data from terrestrial and underwater archaeological survey. The final outcome is the creation of an archaeological predictive model for the reconstruction of ancient landscapes considering both spatial and temporal settlement dynamics.
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Dynamic models for reconstructing ancient coastal landscapes: the use of the MAXENT algorithm. Matteo Lorenzini, Pier Giorgio Spanu. Presented at Society for Historical Archaeology, Leicester, England, U.K. 2013 ( tDAR id: 428203)
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min long: 6.624; min lat: 36.649 ; max long: 18.513; max lat: 47.095 ;
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Contact(s): Society for Historical Archaeology