Association rule mining with earthquake data collected from Turkiye region

TL;DR

This study applies association rule mining to earthquake data from Turkiye over five years, uncovering patterns and relationships between seismic events to aid future geologic and predictive research.

Contribution

It introduces a novel application of association rule mining to seismic data, providing a statistical basis for future machine learning-based analyses.

Findings

Identified prominent association rules in earthquake data

Revealed regional and temporal patterns in seismic activity

Provided a foundation for geologic verification and prediction

Abstract

Earthquakes are evaluated among the most destructive disasters for human beings, as also experienced for Turkiye region. Data science has the property of discovering hidden patterns in case a sufficient volume of data is supplied. Time dependency of events, specifically being defined by co-occurrence in a specific time window, may be handled as an associate rule mining task such as a market-basket analysis application. In this regard, we assumed each day's seismic activity as a single basket of events, leading to discovering the association patterns between these events. Consequently, this study presents the most prominent association rules for the earthquakes recorded in Turkiye region in the last 5 years, each year presented separately. Results indicate statistical inference with events recorded from regions of various distances, which could be further verified with geologic evidence from the field. As a result, we believe that the current study may form a statistical basis for the future works with the aid of machine learning algorithm performed for associate rule mining.