Prediction of Earthquakes in Nepal and the Adjoining Regions Using LSTM - Aryal, Manoj; Tiwari, Ram Krishna; Paudyal, Harihar (BMC Journal of Scientific Research, 2024)
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.