پیش‌بینی زلزله با استفاده از الگوریتم‌های یادگیری ماشین: مروری بر رویکردها و چالش‌ها

In the last few decades, a lot of research has been done to predict earthquakes by using various approaches and technologies. Despite significant advances in seismology and data mining, accurately determining the location and intensity of an earthquake even after it occurs remains a complex and challenging issue in this field. This natural phenomenon requires multidimensional and data-driven analyses due to the complex and nonlinear nature of the processes inside the Earth. However, in recent years, the use of machine learning-based methods has been able to provide new perspectives and significant results in earthquake prediction. By processing and analyzing a wide range of seismic data and geophysical characteristics, these algorithms have the ability to extract patterns and complex relationships between variables, and in this way, they can estimate the probability of future events with more accuracy. This paper presents a review of studies conducted in the field of earthquake prediction using machine learning algorithms. These studies cover various regions of the world, and most of the reviewed models focus on predicting the magnitude, trend, and probability of earthquakes. In this review, the performance of different algorithms and various seismic indices is compared.