Akademik Veri Yönetim Sistemi
JOURNAL OF SEISMOLOGY, 2025 (SCI-Expanded)
This study addresses the critical need for accurate prediction models of seismic activity in Turkey, focusing on the main earthquakes and the aftershocks that follow them. The complex geological structure of Turkey, controlled by major fault lines such as the North Anatolian Fault Line and the East Anatolian Fault Line, requires robust analysis to understand seismic hazards better and to implement effective preventive measures. This research aims to fill the gap in the predictive modeling of integer-valued seismic data by comparing the effectiveness of first-order INteger-valued AutoRegression (INAR(1)) models with the more commonly used AutoRegressive Integrated Moving Average (ARIMA) models. To achieve this, we analysed the occurrence of mainshocks and aftershocks on a monthly basis from January 2011 to December 2020. The INAR(1) models were specifically applied to this integer-valued time-series data, and their forecasts were compared with those produced by ARIMA models. Our results indicate that the INAR(1) models provide forecasts closer to the observed values than the ARIMA models for both the mainshock and aftershock datasets. In particular, the INAR(1) models showed superior performance in terms of accuracy, with numerical results showing a reduction in forecast error of about 15% compared to ARIMA models. These results have significant implications for earthquake preparedness and risk reduction in Turkey. Through the use of INAR(1) models, we can improve the accuracy of the prediction of seismic activity and thereby increase the ability to implement safety measures in a timely and effective manner. This study highlights the importance of better understanding and mitigating earthquake risk by using appropriate statistical models tailored to the specific characteristics of seismic data.