Throughout mankind’s history, the need to secure and protect the home settlement was an essential one. This holds especially true for the city of Ainos (modern Enez) in Turkish Thrace. Due to its continuous settlement history since the 7th/6th century BC, several different types of city walls were built—sometimes even on top of each other—several of which have been preserved over time. To decipher the construction style, the course and the age of a buried city wall segment in the southern part of the former city, a geoscientific multi-proxy approach including magnetic gradiometer (MG) and electrical resistivity tomography (ERT) measurements in combination with granulometrical, sedimentological and microfaunistical investigations on sediment cores was applied. We were able to (1) present reasonable arguments for its Hellenistic age; (2) reveal the course of this wall segment and extrapolate it further north into a less studied area; and (3) demonstrate that in this near-coastal area, the former swampy terrain had been consolidated for constructing the wall. Our multi-proxy approach serves as a valuable example for investigating buried structures in archaeological contexts, avoiding a less-economical, time-consuming, or even forbidden excavation.

We combined biostratigraphical analyses, archaeological surveys, and Glacial Isostatic Adjustment (GIA) models to provide new insights into the relative sea-level evolution in the northeastern Aegean Sea (eastern Mediterranean). In this area, characterized by a very complex tectonic pattern, we produced a new typology of sea-level index point, based on the foraminiferal associations found in transgressive marine facies. Our results agree with the sea-level history previously produced in this region, therefore confirming the validity of this new type of index point. The expanded dataset presented in this paper further demonstrates a continuous Holocene RSL rise in this portion of the Aegean Sea. Comparing the new RSL record with the available geophysical predictions of sea-level evolution indicates that the crustal subsidence of the Samothraki Plateau and the North Aegean Trough played a major role in controlling millennial-scale sea-level evolution in the area. This major subsidence rate needs to be taken into account in the preparation of local future scenarios of sea-level rise in the coming decades.

Geomorphology generally aims to describe and investigate the processes that lead to the formation of landscapes, while geochronology is needed to detect their timing and duration. Due to restrictions on exporting geological samples from Egypt, modern geoscientific studies in the Nile Delta lack the possibility of dating the investigated sediments and geological features by standard techniques such as OSL or AMS ¹⁴C; therefore, this study aims to validate a new approach using machine‐learning algorithms on portable X‐ray fluorescence (pXRF) data. Archaeologically dated sediments from the archaeological excavations of Buto (Tell el‐Fara'in; on‐site) that pXRF analyses have geochemically characterized serve as training data for running and comparing Neural Nets, Random Forests, and single‐decision trees. The established pXRF fingerprints are transferred via machine‐learning algorithms to set up a chronology for undated sediments from sediment cores (i.e., the test data) of the nearby surroundings (off‐site). Neural Nets and Random Forests work fine in dating sediments and deliver the best classification results compared with single‐decision trees, which struggle with outliers and tend to overfit the training data. Furthermore, Random Forests can be modeled faster and are easier to understand than the complex, less transparent Neural Nets. Therefore, Random Forests provide the best algorithm for studies like this. Furthermore, river features east of Kom el‐Gir are dated to pre‐Ptolemaic times (before 332 B.C.) when Kom el‐Gir had possibly not yet been settled. The research in this paper shows the success of close interactions from various scientific disciplines (Geoinformatics, Physical Geography, Archaeology, Ancient History) to decipher landscape evolution in the long‐term‐settled Nile Delta's environs using machine learning. With the approach's design and the possibility of integrating many other geographical/sedimentological methods, this study demonstrates the potential of the methodological approach to be applied in other geoscientific fields.

This paper presents the application of machine-learning techniques on pXRF data to establish a chronology for sediment cores around Tell Buto (Tell el-Fara´in) in the northwestern Nile Delta. As modern laboratories for dating techniques like OSL or 14C are rare in Egypt and sample export is restricted, we are facing a lack of opportunities to create a robust chronology, which is indispensable in modern Geoarchaeology.Therefore, we present a new approach to transfer archaeological age information gained at the excavation at Buto to corings of the wider Buto area. Sediments of archaeological outcrops and pits with known age are measured using pXRF to create a geochemical “fingerprint” for several historic eras. Afterwards, these “fingerprints” are transferred to corings of the surrounding areas using machine-learning algorithms.This paper presents 1) the application of three different machine-learning approaches (Neuronal Net, Random Forest, and C5.0 decision tree) to check if archaeological age information can be transferred to sediments far off the settlement mounds using pXRF data, 2) the comparison of all approaches and the evaluation if the easily anticipated decision tree and Random Forest show similar results as the “black-box system” Neuronal Net, and finally, 3) a case study that provides the results of Altmeyer et al. (in review) for Kom el-Gir, a further settlement mound little north of Buto, with a chronostratigraphic framework based on this approach.Reference:Altmeyer, M., Seeliger, M., Ginau, A., Schiestl, R. & J. Wunderlich (in review):  Reconstruction of former channel systems in the northwestern Nile Delta (Egypt) based on corings and electrical resistivity tomography (ERT). (Submitted to E & G Quaternary Science Journal).