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Pompeii, buried by the explosive A. D. 79 eruption of Somma-Vesuvius, is one of the most studied ancient cities in the Roman world. However, until very recently, the rural settlement in its hinterland had been largely ignored by systematic archaeological research. The ancient landscape around Pompeii consisted of a dense network of Roman farms (villae rusticae). They are believed to have played a vital role in ancient rural life and economy and thus represented the interactive rural-urban relationship in the Sarno River plain. The systematic investigation of published work combined with new fieldwork has yielded a data set of 140 villae rusticae in the Sarno River plain. Geographic information system based spatial statistics as well as predictive modeling were applied to gain a more detailed understanding of the ancient rural settlement structure in relation to the underlying paleoenvironmental and socioeconomic conditions. A high-resolution pre-A. D. 79 paleolandscape model of the Sarno River plain was utilized. The aim of this paper is to address theoretical considerations, the methodological implementation, and the archaeological discussion of the analysis of the ancient rural settlements and agriculture around Pompeii. (C) 2016 Wiley Periodicals, Inc.
In this paper we evaluate different methods to predict soil erosion processes. We derived different layers of predictor variables for the study area in the Northern Chianti, Italy, describing the soil-lithologic complex, land use, and topographic characteristics. For a subcatchment of the Orme River, we mapped erosion processes by interpreting aerial photographs and field observations. These were classified as erosional response units (ERU), i.e. spatial areas of homogeneous erosion processes. The ERU were used as the response variable in the soil erosion modelling process. We applied two models i) bootstrap aggregation (Random Forest: RF), and ii) stochastic gradient boosting (TreeNet: TN) to predict the potential spatial distribution of erosion processes for the entire Orme River catchment. The models are statistically evaluated using training data and a set of performance parameters such as the area under the receiver operating characteristic curve (AUC), Cohen's Kappa, and pseudo R2. Variable importance and response curves provide further insight into controlling factors of erosion. Both models provided good performance in terms of classification and calibration; however, TN outperformed RF. Similar classes such as active and inactive landslides can be discriminated and well interpreted by considering response curves and relative variable importance. The spatial distribution of the predicted erosion susceptibilities generally follows topographic constraints and is similar for both models. Hence, the model-based delineation of ERU on the basis of soil and terrain information is a valuable tool in geomorphology; it provides insights into factors controlling erosion processes and may allow the extrapolation and prediction of erosion processes in unsurveyed areas.