Data-Driven Prediction of Earthquake Parameters Using Historical  Seismic Records and Machine Learning

Anil Khatak, Sheenam Naaz

Data-Driven Prediction of Earthquake Parameters Using Historical Seismic Records and Machine Learning

Authors

Anil Khatak, Sheenam Naaz

Keywords:

AI, Earthquake prediction, Machine learning, Seismic data analysis, Random Forest, Polynomial Regression, Disaster management.

Abstract

Earthquakes are considered to be among the most devastating natural disasters, which often lead toserious human impacts and economic imbalance. Despite the scientific impossibility in taking anaccurate forecast of earthquake occurrence, machine learning has advanced in the calculation of most important seismic parameters

References

Geller, Robert J., David D. Jackson, Yan Y. Kagan, and Francesco Mulargia. "Earthquakes cannot be predicted." Science 275, no. 5306 (1997): 1616-1616.

Reasenberg, Paul A., and Lucile M. Jones. "Earthquake hazard after a mainshock in California." Science 243, no. 4895 (1989): 1173-1176.

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Published

2024-11-15

How to Cite

Anil Khatak, Sheenam Naaz. (2024). Data-Driven Prediction of Earthquake Parameters Using Historical Seismic Records and Machine Learning . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6599–6609. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3847