Year: 2024
Type: article
Abstract: This study applies Long Short-Term Memory (LSTM) model to predict earthquake magnitudes in the frequently earthquake-affected Himalayan country Nepal and the adjoining region. The seismic data on the mb scale, from September 1964 to August 2024, were taken from the International Seismological Centre (ISC) catalogue, spanning rectangular boundary of latitudes 26.30°N to 30.50°N and longitudes 80.00°E to 88.30°E. The methodology involved extensive data preprocessing, feature engineering, and the implementation of a deep learning model. The LSTM network demonstrated moderate predictive power, achieving a Mean Absolute Error (MAE) of 0.2789, Root Mean Square Error (RMSE) of 0.3728, and coefficient of determination (R²) score of 0.4294. The model effectively captured overall seismic trends and showed consistent performance over time. A limitation of the model is its tendency to predict magnitudes within the range of 3.5 to 5.0, resulting in the underestimation of strong earthquakes and slight overestimation of weaker ones.
Institutions: Tribhuvan University, Birendra Multiple Campus
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405874448