Seismic Shift: Predicting Earthquakes With Deep Learning

DOI

10.2991/978-94-6463-858-5_252How to use a DOI?

Keywords

Earthquakes; Deep Learning; Neural Networks; LSTM; Earthquake Prediction; Seismic Data; Disaster Management

Abstract

Earthquakes are a big threat. They kill people, wreck buildings, and mess up economies. We need to predict them well to lower risks and handle disasters better. This project uses deep learning methods, such as neural networks and Long Short-Term Memory (LSTM) models, to guess earthquake sizes and depths. It looks at seismic data from the USGS Earthquake Database. The team made and tested several models checking their Mean Squared Error (MSE) and Mean Absolute Error (MAE). The final output demonstrates that the LSTM model does the best job. It catches time-based patterns and makes good guesses. There are still problems to solve, but this way of doing things looks promising for earthquake prediction. It shows we need to keep studying to make our guesses more accurate and reliable.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. Asha
AU  - P. Durga Valli Devi
AU  - A. Shreya
AU  - N. Manisha
AU  - K. Chandra Hasini
PY  - 2025
DA  - 2025/11/04
TI  - Seismic Shift: Predicting Earthquakes With Deep Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3005
EP  - 3017
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_252
DO  - 10.2991/978-94-6463-858-5_252
ID  - Asha2025
ER  -