Predicting Earthquakes Across The Anatolian Plate Through Machine Learning Algorithms

Abstract

Situated along three major plate boundaries, Anatolian plate is represented by major destructive earthquakes exceeding Mw 7. Accurate forecasting of earthquake epicenters is crucial for both structural resilience and efficient risk reduction. In this work, we develop a machine-learning based epicenter prediction framework covering the entire territory of Türkiye, using the national seismic catalogue provided by KOERİ and AFAD. The dataset in particular is partitioned into four consistent clusters derived from localized strain fields estimated through integrated InSAR and GNSS observations (e.g Weiss et al 2020). For training the models, we removed the background max shear strain 50 nanostrain/year and consider, namely, the North Anatolian fault system, East Anatolian fault system, western Anatolian extensional region, and East Anatolian shortening zone. In addition, all earthquakes are classified as large or medium using a magnitude threshold of Mw ≥ 5, yielding eight distinct datasets. For each dataset, we train and evaluate seven machine-learning models—Linear Regression, Random Forest, XGBoost, Multilayer Perceptron (MLP), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to predict future epicenters (latitude and longitude) from historical spatiotemporal information. Comparing all models within each geodetic cluster allows us to identify which model families perform better under specific tectonic deformation regimes, while simultaneously revealing which regions exhibit higher predictability. This multi-model, multi-region evaluation provides new insights into data-driven seismic forecasting across the Anatolian plate where the role of various plate boundary scale faults (shear zones) are associated with destructive earthquakes.