Finding the Optimal Prediction of the Occurrence of Earthquakes Using Markov Chains and Artificial Intelligence Methods
Abstract
In this research, a method for predicting earthquakes was presented, where seismic data for the Syrian coastal region were studied for the period from 1996 to 2011, which included the earthquake strength, intensity, location and date of occurrence. In light of the analysis of this data, a method for prediction using Markov chains was determined for a specific period. After that, this process was improved using one of the artificial intelligence methods, which is the machine learning method using the random forest method. Due to this improvement, better accuracy in the results was obtained.The research presents an advanced approach to earthquake predictions by integrating artificial intelligence techniques with Markov chains, thus obtaining the proposed predictions that enable communities to take better preventive measures and remove risks from the population. This will lead to reducing losses in lives, property and infrastructure as much as possible. This is the goal of all studies related to predicting earthquakes in the world and of all types
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