Earthquake Forecasting in Kyrgyzstan using Machine Learning: A Random Forest Approach

This study is focused on earthquake forecasting in Kyrgyzstan using machine learning (ML). Earthquake are among the most devasting natural disasters, often resulting in extensive destruction and significant loss of life. Predicting earthquakes can help in reducing the damage caused by earthquakes and save human lives. ML method analyze massive datasets and generate different models over time with focusing on performance. ML method called Random Forest (RF) to predict earthquakes in Kyrgyzstan region is used to experiences frequent earthquakes. We used data such as earthquake size, depth, location, timing, and distance from fault lines to train the model. The RF method was also able to find out the patterns present in the data and predict places, times for earthquakes. Most importantly, this method was able to identify factors causing earthquakes, tectonic stress, and the activity of a fault line. This research shows the improvement in earthquake prediction by using RF method so that there may be better warning system for Kyrgyzstan. Future research will involve the improvement of the model to include real-time data and expand its application to earthquakeprone regions worldwide.