Earthquake Predictions using Time Series Analysis is

Authors

DOI:

https://doi.org/10.48001/978-81-966500-9-4_14

Keywords:

Earthquake Prediction, Earthquake Prediction using Time Series, Disentangling Seismic Tremor Forecast

Abstract

As of late, there have been huge headways in utilizing man-made brainpower (simulated intelligence) to foresee quakes. Specialists at The College of Texas at Austin fostered an artificial intelligence calculation that had the option to foresee 70% of tremors seven days before they happened during a preliminary in China. The man-made intelligence was prepared to identify factual examples in seismic information and effectively anticipated 14 tremors inside a 200-mile range of their assessed area with practically careful strength. The scientists intend to additionally test the computer-based intelligence in Texas and at last coordinate it with material science-based models to make a summed-up framework that can be applied anyplace on the planet.
One more way to deal with tremor expectation is utilizing the force of time series examination. An exploration paper named "Disentangling Seismic Tremor Forecast: A Period Series Examination Approach" proposes a one-of-a-kind and imaginative way to deal with quake expectations utilizing time series investigation. The review means to add to the improvement of more exact and solid tremor forecast models by uncovering stowed-away examples inside seismic time series information. The proposed philosophy includes procuring broad seismic time series datasets incorporating different locales and levels of seismic action, trailed by thorough preprocessing and highlight designing to remove significant data. Best-in-class time series examination procedures, including autoregressive models, Fourier changes, and repetitive brain organizations, are then applied to uncover fleeting conditions and patterns inside the information. Consolidating geospatial data, land information, and natural factors further improves the models’ prescient power.
The fundamental goal of the exploration is to foster a prescient system fit for assessing tremor probabilities throughout various time skylines. The aftereffects of this exploration might offer important experiences for early advance notice frameworks, catastrophe readiness, and chance alleviation procedures, eventually lessening the effect of seismic occasions on living souls and foundations. The review plans to overcome any issues between information-driven science and geophysical investigation, making ready for another period in seismic tremor expectation. 

Downloads

Download data is not yet available.

References

Preethi, G., and Santhi, B. (2011). Study on techniques of earthquake prediction. International Journal of computer applications, 29(4), 55-58

Amei, A., Fu, W., and Ho, C. H. (2012). Time series analysis for predicting the occurrences of large-scale earthquakes. International Journal of Applied Science and Technology, 2(7).

Shah, H., Ghazali, R., and Nawi, N. M. (2011). Using artificial bee colony algorithm for MLP training on earthquake time series data prediction. arXiv preprint arXiv:1112.4628.

Otari, G. V., and Kulkarni, R. V. (2012). A review of application of data mining in earthquake prediction. International Journal of Computer Science and Information Technologies, 3(2), 3570-3574.

Lyubushin, A. A. (1999). Analysis of multidimensional geophysical monitoring time series for earthquake prediction.

Morales-Esteban, A., Martínez-Álvarez, F., Troncoso, A., Justo, J. L., and Rubio-Escudero, C. (2010). Pattern recognition to forecast seismic time series. Expert systems with applications, 37(12), 8333-8342.

Ali, A., Ghazali, R., and Deris, M. M. (2011, December). The wavelet multilayer perceptron for the prediction of earthquake time series data. In Proceedings of the 13th International Conference on Information Integration and Web-based applications and services (pp. 138-143).

Barkat, A., Ali, A., Hayat, U., Crowley, Q. G., Rehman, K., Siddique, N., ... and Iqbal, T. (2018). Time series analysis of soil radon in Northern Pakistan: Implications for earthquake forecasting. Applied Geochemistry, 97, 197-208.

Downloads

Published

2023-12-30

How to Cite

Raj , N. ., & Tiwari , S. . (2023). Earthquake Predictions using Time Series Analysis is. QTanalytics Publication (Books), 166–183. https://doi.org/10.48001/978-81-966500-9-4_14