Finding Earthquake Patterns with Time Series and Regression Models

Due to their unpredictability, earthquakes are certainly the most dangerous natural disasters. Scientists have long struggled to understand the movement of tectonic plates, as they occur at depths that cannot be directly seen and occur extremely slowly, at a rate that results in possibly one every thousand years between two adjacent plates. What if we could take a different approach, however? What if we could leverage the recent advances in machine learning to close this gap? That is exactly what we aimed to do in our research, with the question of whether various factors related to earthquakes, such as their magnitude and time of occurrence, could be determined through the analysis of years of historical data. This was conducted through the analysis of a dataset which contained records of all major earthquakes (magnitude 5.5 or higher) from 1900-2023. The library scikit-learn was used for various regression models, such as linear regression, random forest regression, and lasso regression, and the library FB prophet was used for time series forecasting, a method used to analyze patterns in previous earthquakes to predict new ones. Our conclusions were that, with some degree of error, the magnitude of an earthquake can be determined based on location/time and the time of occurrence can be pinpointed to specific days of the week as well as months.