Machine Learning (Neuronal Net, Random Forest, and C5.0 single decision tree) based on pXRF data as a tool to date sediment layers of the Nile Delta

Domaine:

geospatial

Type de record:

paper

Créateur:

MarMarAndRob

Éditeur:

Cop

Hôte:

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).

Visit

doi.org

Similaires

Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural NetworksAnalisis Komparatif Algoritma Decision Tree dan Random Forest dalam Prediksi Kesesuaian Lahan Pertanian Berbasis Machine Learning.A copulative decision tree as a writing tool for SepediC5.0 Decision Tree Classification Algorithm for Risk Prediction Among Pregnant Women in Ughelli North LGA of Delta State of NigeriaCombining Machine Learning and Process-Based Modelling for Sediment Load Estimation in the Data-Scarce Kessie Watershed, Upper Blue Nile BasinMachine Learning-Based Sedigraph Reconstruction for Enhanced Sediment Yield Estimation in the Upper Blue Nile Basin

Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the

Analisis Komparatif Algoritma Decision Tree dan Random Forest dalam Prediksi Kesesuaian Lahan Pertanian Berbasis Machine Learning.

Perubahan iklim, anomali cuaca, dan degradasi kualitas tanah menjadi tantangan utama dalam sektor pe

A copulative decision tree as a writing tool for Sepedi

C5.0 Decision Tree Classification Algorithm for Risk Prediction Among Pregnant Women in Ughelli North LGA of Delta State of Nigeria

Combining Machine Learning and Process-Based Modelling for Sediment Load Estimation in the Data-Scarce Kessie Watershed, Upper Blue Nile Basin

The Upper Blue Nile Basin contributes about 60% of the Nile River’s annual streamflow but faces seve

Machine Learning-Based Sedigraph Reconstruction for Enhanced Sediment Yield Estimation in the Upper Blue Nile Basin

Sediment-laden runoff in Ethiopia’s Upper Blue Nile Basin (UBNB) threatens the ecological balance of