Advancing Earthquake Risk Reduction through Machine Learning Enhanced Early Warning Systems

Ramakrishnan, Pranav (2024) Advancing Earthquake Risk Reduction through Machine Learning Enhanced Early Warning Systems. Masters thesis, Dublin, National College of Ireland.

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Abstract

Earthquakes are one of the most dangerous natural disasters.it is very important to manage the risk by making accurate predictions for an effective early warning system. This project uses the historical seismic data for the prediction of earthquake magnitude using different machine learning models. The aim is to determine the earthquake magnitude with the help of the machine learning algorithms integrated into the PyCaret Framework. Traditional approaches apply basic statistical patterns, which may fail to capture many-factor patterns inherent in earthquake data. In this research, we defined several features, like x-magnitude and then attempted to predict y-magnitude using our model and checked the accuracy of the model. The dependent variables were evaluated with Mean Absolute percentage Error, Mean Squared Error , R-squared . The findings were that the Lasso regression performed best, the results are MSE 0.240, MAPE 7.01% and R-square -4.414200827151937e-05, which were more accurate than older ways of estimating. The present work points out that there is hope in developing the application of machine learning to make better forecasts of earthquakes. In future, we aim to add more features and find ways that can help predicting earthquakes for better results.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Qayum, Abdul UNSPECIFIED
Uncontrolled Keywords:	Earthquake prediction; machine learning; early warning systems; PyCaret model; risk reduction; earthquake magnitude forecasting; predictive modeling; disaster mitigation; seismology; stremlit
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GE Environmental Sciences > Earth sciences > Geology > Physical geology > Sedimentation and deposition > Earth movements > Earthquakes Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 13:05
Last Modified:	04 Sep 2025 13:05
URI:	https://norma.ncirl.ie/id/eprint/8785

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