Supervisor
Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This research investigates the application of machine learning regression models to improve earthquake prediction by integrating geophysical and astronomical factors such as Earth-Moon gravitational forces, their varying distances, and localized gravity fluctuations. Using data from 2011 to 2024, sourced from the US Geological Survey (USGS) and web scraping, the study tested models across four dataset proportions (25%, 50%, 80%, and 100%) with a 70:30 train-test split. The XGBRegressor model emerged as the best performer, achieving an R² score of 0.8706 on training data and 0.8632 on test data, along with a Mean Squared Error (MSE) of 0.1114 and Mean Absolute Error (MAE) of 0.2473. Feature importance analysis via Information Gain, ANOVA, and Lasso identified gravity variations as significant predictors, while Moon-Earth distance showed no statistically significant impact. Despite promising results, the moderate R² score highlights the model's limitations in capturing the full complexity of earthquake prediction. Future research should incorporate more geological metrics, real-time seismic data, and advanced machine learning techniques, such as convolutional and recurrent neural networks, to enhance predictive capabilities.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Kushwaha, Aadarsh, "ML Predictive Model for Earthquakes Integrating Mass, Distance, Gravity, and Magnitude." (2024). ICT. 63.
https://arc.cct.ie/ict/63