Comparing different machine‐learning techniques to date Nile Delta sediments based on portable X‐ray fluorescence data. Issue 1 (1st November 2022) - Seeliger, Martin; Ginau, Andreas; Altmeyer, Marina; Neis, Pascal; Schiestl, Robert; Wunderlich, Jürgen
Abstract: 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 14 C; 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. Highlights: Neural Nets and Random Forest yield excellent results in classifying sediments C5.0 single‐decision trees showed substantial deficits in this context