Application of machine-learning algorithms for tephrochronology: a case study of Plio-Quaternary volcanic fields in the South Aegean Active Volcanic Arc

We performed several machine-learning algorithms on a geochemical dataset including whole-rock (n = 1656) and glass (n = 1092) compositions of lavas and pyroclastics belonging to 8 volcanic fields along the South Aegean Active Volcanic Arc (SAAVA). We did not only test our trained model with the unk...

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Vydané v:	Earth science informatics Ročník 15; číslo 2; s. 1167 - 1182
Hlavní autori:	Uslular, Göksu, Kıyıkçı, Fatih, Karaarslan, Enis, Kuşcu, Gonca Gençalioğlu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022 Springer Nature B.V
Predmet:	Algorithms Artificial neural networks Datasets Decision making Decision trees Discriminant analysis Earth and Environmental Science Earth Sciences Earth System Sciences Geochemistry Geochronology Information Systems Applications (incl.Internet) Machine learning Mathematical models Modelling Neural networks Ontology Probabilistic models Quaternary Research Article Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Support vector machines Training Volcanic activity Volcanic fields Volcanoes Gradient boosting algorithms Imbalanced data Tephrochronology Machine-learning South Aegean Active Volcanic Arc
ISSN:	1865-0473, 1865-0481
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