In recent years, the realm of seismology has witnessed an increased integration of
advanced computational techniques, seeking to enhance the precision and timeliness of …

Abstract Machine learning has advanced rapidly in the last decade, promising to
significantly change and improve the function of big data analysis in a variety of fields. When …

In the last few years, deep learning has solved seemingly intractable problems, boosting the
hope to find approximate solutions to problems that now are considered unsolvable …

In the recent years, the seismological community has adopted deep learning (DL) models for
many diverse tasks such as discrimination and classification of seismic events, identification …

Earthquakes usually cause severe injuries and loss of life, so researchers have developed
various methods to predict them. However, the prediction accuracies of these methods are …

One of the most expensive natural disasters that affect people, earthquakes occur suddenly.
As a result, earthquake prediction has grown in importance and difficulty for humanity …

Realistically predicting earthquake is critical for seismic risk assessment, prevention and
safe design of major structures. Due to the complex nature of seismic events, it is …

Reliable earthquake forecasting methods have long been sought after, and so the rise of
modern data science techniques raises a new question: does deep learning have the …

Earthquakes are one of nature's most devastating disasters. Earthquake prediction is critical
in seismology since its success can save lives, property, and infrastructure. Numerous …

INTRODUCTION: This research paper presents an innovative method that merges neural
networks and random forest algorithms to enhance earthquake prediction. OBJECTIVES …