2 Ağustos 2023 Dergi makalesi Açık Erişim

Hatice Öncel Çekim; Hatice Nur Karakavak; Gamze Özel; Senem Tekin

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Hatice Öncel Çekim</dc:creator>
  <dc:creator>Hatice Nur Karakavak</dc:creator>
  <dc:creator>Gamze Özel</dc:creator>
  <dc:creator>Senem Tekin</dc:creator>
  <dc:date>2023-08-02</dc:date>
  <dc:description>The Aegean region is geologically situated at the western end of the Gediz Graben system, influenced by the Western Anatolian Regime. In addition, the region is characterized by various active fault lines that can generate earthquake activity. Numerous earthquakes have been recorded in the region, causing significant material and moral damage from the past to the present. In this study, earthquake data from three different catalogs are examined. The non-clustered catalog is compiled for the years 1970 to 2020, including earthquakes with a moment magnitude (Mw) greater than 3.0. The monthly average magnitudes of earthquakes in the region are obtained and analyzed using ARIMA, singular spectrum analysis (SSA), and deep learning methods including convolutional neural network (CNN) and long short-term memory (LSTM), as these methods have not been compared for the region previously. Each method has a different benefit. ARIMA analyzes time series trends and seasonal patterns, while SSA focuses on decomposition and feature extraction. LSTM attempts to capture complex relationships using memory mechanisms, while CNN is powerful at pattern recognition and extracting important features. Thanks to this diversity, our study allows for more comprehensive and reliable forecasts of average earthquake magnitudes for the next 36 periods. The estimation capabilities and error rates of each method were analyzed based on earthquake magnitude data, and it was determined that the LSTM method provided the most effective and accurate predictions.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/263704</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:263704</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by-sa</dc:rights>
  <dc:subject>ARIMA</dc:subject>
  <dc:subject>Deep learning</dc:subject>
  <dc:subject>Earthquake</dc:subject>
  <dc:subject>Long short-term memory</dc:subject>
  <dc:subject>Magnitude</dc:subject>
  <dc:subject>Neural network</dc:subject>
  <dc:subject>Singular spectrum analysis</dc:subject>
  <dc:title>Earthquake magnitude prediction in Turkey: a comparative study of deep learning methods, ARIMA and singular spectrum analysis</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>