Makine öğrenimi yöntemleri kullanılarak Türkiye’nin kuzeybatısı için deprem tahmini

Year 2023, , 166 - 178, 24.08.2023

Ayhan Doğan

https://doi.org/10.17824/yerbilimleri.1325321

Abstract

Depremleri önceden tahmin edebilmek insan yaşamı ve konforu için önemli bir konu olmuştur. Ancak karmaşık bir mekanizmaya sahip olan depremleri tahmin edebilmek oldukça zordur. Geçmişte depremleri tahmin edebilmek için farklı yöntemler kullanılırken son zamanlarda yapay zeka yöntemlerindeki gelişmelerle birlikte deprem tahminleri yapabilmek için bu yöntemler de kullanılmaya başlanmıştır. Bu çalışmada Türkiye’nin kuzeybatı bölgesinde gelecekte olma ihtimali bulunan, 6 ve üzeri büyüklükteki depremlerin odak koordinatları ve odak derinlikleri tahmin edilmeye çalışılmıştır. Bu çalışmada karşılaştırmalı olarak altı farklı makine öğrenimi yöntemi (Destek Vektör Makineleri, Lineer Regresyon, Gradient Boost, Elastic Net, Bayesian Ridge ve XGBoost) kullanılmış ve tahmin sonuçları karşılaştırılmıştır. Sonuçlar RMSE, MAE ve Düzeltilmiş R2 performans metrikleriyle değerlendirilmiştir. Tahmin sonuçları gelecekte Türkiye’nin kuzeybatı bölgesinde Bursa ili sınırları içerisinde İznik Gölü’nün kuzeyinden başlayarak batıya doğru Ekinli, İmralı adasının kuzeybatısı, Avşa adasının kuzeyi ve Marmara adasının kuzeybatısında 6 ve üzerinde depremler olabileceğini göstermektedir.

Keywords

Makine öğrenimi, Deprem tahmini, Türkiye’nin kuzeybatısı, odak konumu

Earthquake prediction for the northwest of Turkey with machine learning methods

Abstract

Being able to predict earthquakes has been an important issue for human life and comfort. However, earthquakes with a complex mechanism are quite difficult to predict. While different methods were used to predict earthquakes in the past, these methods have been used to make earthquake predictions with the developments in artificial intelligence methods recently. In this study, the focal coordinates and focal depths of earthquakes with a magnitude of 6 and above, which are likely to occur in the northwestern region of Turkey, were tried to be estimated. In this study, six different machine learning methods (Support Vector Machines, Linear Regression, Gradient Boost, Elastic Net, Bayesian Ridge and XGBoost) were used comparatively and the prediction results were compared. Results were evaluated with RMSE, MAE, and Adjusted R2 performance metrics. The estimation results show that earthquakes of 6 or more may occur in the future in the northwestern region of Turkey, within the borders of Bursa province, starting from the north of Lake Iznik and going westwards in Ekinli, northwest of İmralı island, north of Avşa island and northwest of Marmara island.

Keywords

Machine learning, Earthquake prediction, Northwest of Turkey, Hypocenter location, Hypocenter depth

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