Предиктивное археологическое моделирование методом максимальной энтропии (на примере поселений позднеримского времени в бассейне Верхнего Дона)

Abstract

The paper is devoted to an archaeological predictive modeling technique with GIS and machine learning based on maximal entropy algorithm (MaxEnt). Archaeological sites of the late roman time presented by settlements of Kashirka-Sedelok type (mid-3rd — early 4th century AD) in the Upper Don basin were the modeling objects. We found out that there are 169 known sites of such type in the study region, and collected all available information on their locations into a geodatabase including our own field survey data. The coordinates of these sites split into training and testing subsets served for developing a predictive model with MaxEnt. Environmental GIS-layers based on a digital elevation model and thematic maps were the model predictors. We made such predictors to reflect the paleogeography of the Upper Don basin in the study time period by choosing relatively stable landscape features of relief, bedrock, climate, hydrography and soils of the area. The trained model analyzed through more than 7 million cells of 90x90 m with overall area of 59 400 sq. km to identify potentially suitable areas for the Kashirka-Sedelok settlements and found 29 860 ha the most suitable areas (less than 1 % of the study area) as the result. The efficiency of the conducted modeling, evaluated on the testing sites, is high based on metrics of receiver operating characteristic (AUC = 0,915) and Kvamme’s Gain (KG = 0,97). The results of modeling serve as an agenda for the upcoming archaeological surveys in the region.