GIS-BASED COMPARATIVE ARCHAEOLOGICAL PREDICTIVE MODELS: A FIRST APPLICATION TO IRON AGE SITES IN THE BEKAA (LEBANON)

Abstract

This study tests the use of Frequency Ratio (FR), Statistical Index (Wi), and Binary Logistic Regression (BLR) methods for establishing predictive maps for Iron Age sites in the Bekaa (Lebanon). As such it stands as the first attempt to use archaeological predictive modelling on a national level. The models were generated using an archaeological database consisting of 42 Iron Age I and 30 Iron Age II sites located in the Bekaa valley in Lebanon and six geo-environmental factors: distance to rivers, distance to cropland, slope, aspect, elevation, and terrain texture. The accuracy and predictive capacity of these models were tested using Kvamme’s Gain value. The results indicate that the FR method is more reliable in locating areas of archaeological potential than Wi and BLR. The analysis of the FR- and Wi-based models shows that distance to rivers, terrain texture, and elevation provide the most significant classes affecting settlement incidence. On the other hand, in the BLR, distance to crops and distance to rivers are the most statistically significant explanatory variables for identifying areas with high archaeological probability. The archaeological predictive maps produced in this study form a valuable tool for cultural heritage management and any future archaeological investigation of the Bekaa region.