Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with Machine Learning Algorithms

ÇOBAN K. H., Sayil N. L.

JOURNAL OF EARTHQUAKE ENGINEERING, cilt.27, sa.9, ss.2533-2554, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 9
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/13632469.2022.2120114
  • Dergi Adı: JOURNAL OF EARTHQUAKE ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2533-2554
  • Anahtar Kelimeler: Machine learning algorithms, regression trees, support vector machines, magnitude, Regression, ANATOLIAN FAULT ZONE, NEURAL-NETWORK APPROACH, ARTIFICIAL BEE COLONY, LOGISTIC-REGRESSION, HAZARD ANALYSIS, SEISMIC HAZARD, MOTION DATA, M-S, OPTIMIZATION, PARAMETERS
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

In this study, three new regression models are created for magnitude-type conversion with different machine learning algorithms (linear regression, regression trees, support vector machines, Gaussian process regression models, ensembles of trees) by using the earthquakes (M >= 4.0) that occurred in Turkey (1900-2020). Additionally, eight new equations are formed with linear and orthogonal regression methods. Developed equations and models are compared to equations selected from the literature by test data. As a result of the study, it is observed that machine learning algorithms create better models and provide results closer to the real values than created and selected equations.