Vibration of Time: Earthquake Magnitude Prediction Using Machine Learning and Graphical Representation of Earthquakes from 1900 to 2023
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DOI:
https://doi.org/10.5281/zenodo.15038498Keywords:
Earthquake Prediction, Machine Learning, Risk Analysis, Artificial IntelligenceAbstract
An earthquake is a natural disaster that significantly impacts human life and structures. This
study aims to contribute to the understanding of this important issue through a comprehensive evaluation
of earthquakes from geological, seismological, and engineering perspectives. Hypotheses developed by
assessing the effects of plate tectonics, volcanic activities, and anthropogenic triggers on earthquakes
analyze the formation process and risk profiles of earthquakes. The strategic importance of determining the
risk profiles of geographical regions in terms of earthquake potential has been emphasized, with a focus on
earthquake zones and hazard analyses.
The dataset, consisting of earthquakes from 1900 onwards, was used as a sample, and various machine
learning models were applied to this data. Models used include Random Forest, Gradient Boosting,
XGBoost, Linear Regression, Ridge Regression, Lasso Regression, and Support Vector Regression. The
performance of these models in predicting earthquake magnitude was compared, and it was found that the
XGBoost model showed the best performance with the lowest Mean Squared Error (MSE).
The results demonstrate that machine learning models have significant potential in predicting earthquake
magnitudes. This study aims to evaluate community preparedness for earthquakes by addressing the role
of exploratory data analysis with artificial intelligence in earthquake risk analysis and prediction. By
providing a multifaceted analysis of earthquakes, this study makes an important contribution to the
academic literature.
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