Forecasting Earthquake Using Machine Learning

Allan Basilio Asma; Peter John Berces Aranas

doi:10.55529/jaimlnn.43.31.40

Forecasting Earthquake Using Machine Learning

https://doi.org/10.55529/jaimlnn.43.31.40

Authors

Allan Basilio Asma Graduate School, Polytechnic University of the Philippines, Manila, Philippines.
Peter John Berces Aranas School of Statistics, University of the Philippines, Diliman, Philippines.

Keywords:

Machine Learning and Neural Network DNN, CNN, RNN, LSTM and CNN LSTM.

Abstract

Many lives and properties were lost in the past due to unforeseeable deadly earthquakes in the Philippines, which encouraged the researcher to examine different models to achieve the best model utilizing machine learning to forecast earthquakes. The researcher employed ARIMA as the baseline model for DNN, RNN, LSTM CNN, and CNN+LSTM then compared neural networks to determine which model had the lowest error using MEA - mean absolute error. After comparing the MEA from the various models, LSTM had the lowest mean absolute error, implying that it is the best model for forecasting earthquakes.

Published

2024-04-01

How to Cite

Allan Basilio Asma, & Peter John Berces Aranas. (2024). Forecasting Earthquake Using Machine Learning . Journal of Artificial Intelligence,Machine Learning and Neural Network (JAIMLNN) ISSN: 2799-1172, 4(03), 31–40. https://doi.org/10.55529/jaimlnn.43.31.40