Association rule mining with earthquake data collected from Turkiye region
This work addresses earthquake prediction and analysis for researchers and disaster management, but it is incremental as it applies an existing method to new data without major methodological innovations.
The study tackled the problem of discovering hidden patterns in earthquake data from the Turkiye region by applying association rule mining, similar to market-basket analysis, to seismic events over the last 5 years, resulting in the identification of prominent association rules for each year separately.
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.