Reconstructing the Roman topography and environmental features of the Sarno River Plain (Italy) before the AD 79 eruption of Somma–Vesuvius

Abstract

A methodology was developed to reconstruct the Roman topography and environmental features of the Sarno River plain, Italy, before the AD 79 eruption of the Somma–Vesuvius volcanic complex. We collected, localized and digitized more than 1800 core drilling data to gain a representative network of stratigraphical information covering the entire plain. Besides other stratigraphical data including the characteristics of the pre-AD 79 stratum, the depth to the pre-AD 79 surface was identified from the available drilling documentations. Instead of a simple interpolation method, we used a machine based learning approach based on classification and regression trees to reconstruct the pre-AD 79 topography. We hypothesize that the present-day topography reflects the ancient topography and related surface processes, because volcanic deposits from the AD 79 eruption coated the ancient landscape. Thus, ancient physiographic elements of the Sarno River plain are still recognizable in the present-day topography. Therefore, a high-resolution, present-day digital elevation model (DEM) was generated. A detailed terrain analysis yielded 15 different primary and secondary topographic indices. Subsequently, a classification and regression model was applied to predict the depth of the pre-AD 79 surface combining present-day topographic indices with other physiographic data. This model was calibrated with the measured depth of the pre-AD 79 surface. The resulting pre-AD 79 DEM was compared with the classified characteristic of the pre-AD 79 stratum, identified from the drilling documentations. This allowed the reconstruction of pre-AD 79 environmental features of the Sarno River plain such as the ancient coastline, the paleo-course of the Sarno River and its floodplain. To the knowledge of the authors, it is the first time that the pre-AD 79 topography of the Sarno River plain was systematically reconstructed using a detailed database and sophisticated data mining technologies.